Device and method for inducing relaxation in user

Abstract

To provide a device, method and system for promoting sleep.SOLUTION: A system may include a monitor such as a non-contact motion sensor from which sleep information may be determined. User sleep information, such as sleep stages, hypnograms, sleep scores, mind recharge scores and body scores, may be recorded, evaluated and / or displayed for a user. The system may further monitor ambient and / or environmental conditions corresponding to sleep sessions. Sleep advice may be generated based on the sleep information, user queries and / or environmental conditions from one or more sleep sessions. Communicated sleep advice may include contents to promote good sleep habits and / or detect risky sleep conditions. Any one or more of a bedside unit sensor module, a smart processing device, such as a smart phone or smart device, and network servers may be implemented to perform the methodologies of the system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application is a continuation of Australian Provisional Patent Application No. 201390 filed on 8 July 2013 No. 2516, U.S. Provisional Patent Application No. 62 / 018,289 filed June 27, 2014 No. 29 / 490,436, filed May 9, 2014. The benefit of the filing date of these applications is claimed. The disclosures of these applications are incorporated herein by reference. It shall form a division.

[0002] The present technology relates to systems and methods for sleep management, including systems that assist users in falling asleep. The present invention may relate to systems and methods. [Background technology]

[0003] Inadequate sleep is a significant problem worldwide, affecting up to 60% of the adult population. Inadequate rest leads to poor performance at work. Fatigue can affect both work and leisure activities. In both cases, the possibility of an accident increases.

[0004] Sleep can be characterized by four distinct stages (phases) that change throughout the night. Yes, sleepers generally cycle through these states.

[0005] Typically, you move from NREM stage 1 to REM stage 3, then repeat this cycle. There are several cycles of the state (3-5 times per night), each lasting about 90 minutes. As discussed later in this specification, the REM stage lasts approximately 110 minutes. can be characterized by so-called rapid eye movements.

[0006] Stages 1-3 are known as non-REM (NREM) or restful sleep. New guidelines from the American Academy of Sleep Medicine divide NREM into three stages: N1, N2, N3, N4, N5, N6, N7, N8, N9, N10, N11, N12, N13, N14, N15, N16, N17, N18, N19, N20, N21, N22, N23, N24, N25, N26, N27, N28, N29, N30 Typically, sleepers transition to REM. Before you go to sleep, you briefly move from deep sleep to light sleep. These stages are as follows: I can understand.

[0007] Stage 1 ("N1"): · Transition between wakefulness and sleep states. Loss of awareness of surroundings (feeling drowsy when not fully awake) This state can be easily aroused. May experience generalized or localized muscle contractions associated with intense visual imagery do. · Falling asleep usually lasts 5 to 10 minutes.

[0008] Stage 2 ("N2"): Sleeping, but not particularly deep (it's difficult to wake up from this stage) is easy). Typically lasts 10 to 25 minutes at a time. Typically, about half of the night is spent sleeping in this state. During this sleep stage, your heart rate, breathing, and brain activity slow down, and your body becomes completely relaxed. It's happening.

[0009] Stage 3 ("N3") - SWS, formerly known as Stages 3 & 4 (Iber et al. 2 007): Deep slow-wave sleep (SWS), which is considered the time when the body regenerates and restores itself. It is being done. After falling asleep, it may take up to 30 minutes to reach this deepest part of sleep. Awakening requires much more effort. Breathing becomes more regular, blood pressure decreases, and pulse rate slows. The amount of deep sleep changes with age (Dijk 2010). As we age, deep sleep decreases (and lighter sleep increases). As we age, the amount of time we spend sleeping tends to decrease. and are more likely to wake up during the night (i.e., stay in light sleep for longer and (and therefore may be more easily disturbed by noise, bedmate movement, discomfort, etc.) This is normal, and most older people will continue to sleep the way they do.

[0010] Rapid eye movement (REM): ○ The eyeballs move under the closed eyelids, and most dreams are seen at this time. While paralyzed, thoughts race through your mind. o This stage is thought to promote learning and memory. If you wake up from this state, you tend to remember that you were dreaming. This is especially likely when M is followed by light sleep (i.e., starting a new cycle). There is. The initial REM period may last only about 5 minutes, but it gradually gets longer throughout the night. The final period will be up to 30 minutes long. REM sleep predominates in the last third of the night. ○In REM, there are more changes in breathing patterns compared to slow wave sleep.

[0011] Healthy sleep Healthy sleep is essential for a healthy life. This increases the risk of diabetes, obesity, depression, high blood pressure, and even stroke.

[0012] Most healthy adults need 7-9 hours of sleep, with experts recommending 8 hours. Some people only need 6 hours of quality sleep, while others need 10 hours of quality sleep. According to a 2009 study from the University of California, genetically While there are people who can get by on just six hours of sleep, this only applies to 3% of the population. It has been suggested that most people, at some point in their lives, Difficulty falling asleep or staying asleep, especially during times of stress or change It is normal to be awake about 5% of the time during the night. All sleep stages are important. However, the balance of deep, light, and REM sleep is best in the morning. This is necessary for a sense of belonging (Epstein & Mardon 2006).

[0013] A graph of sleep stages is called a hypnogram (contour It is called "sleeping architecture" because it looks like a silhouette of a city skyline. (Sometimes this happens.)

[0014] "Sleep efficiency" provides a metric for how well a person slept. This is understood to be the percentage of time spent sleeping in bed each night. A person may spend eight hours in bed, but only four of those hours sleep. If not spent, sleep efficiency can be as low as 50%. , which is based on the premise that people go to bed to get sleep.

[0015] Effects on sleep There are many publications devoted to sleep-related issues. It can affect important things like sleep, productivity, and overall mood. , which can make people obese and lead to health complications such as diabetes (Ostrow 2012; Patel 2 006). When deep sleep is restricted, people tend to feel revitalized no matter how long they stay in bed. If you are sleep deprived, you may go through light sleep and move quickly into deep sleep. It is thought that people tend to spend more time in restorative deep sleep. After deprivation, if sleep is undisturbed, people enter REM earlier (and stay asleep longer). tend to remain in this state for a long time.

[0016] The literature suggests that the only known way for adults to increase the amount of deep sleep they get is through exercise. It has been stated (Epstein & Mardon 2006).

[0017] Alcohol can make you feel drowsy and help you fall asleep, but it suppresses REM sleep. However, after a few hours it is metabolized, which may lead to more alertness.

[0018] You no longer feel fatigued (but your decision-making may be impaired) It is believed that a point of severe fatigue (i.e., fatigue that is not present in the body) can be reached. It may be possible, but your overall health may be affected.

[0019] Buysse et al. (2010), “Can an improvement in sleep positive impact on health? (Sleep Medicine Reviews 14) states that "numerous studies have reported that sleep duration and various health problems such as cardiovascular events, risk of stroke, concomitant arteriosclerosis, and changes in inflammatory markers. "..."The long-term consequences and causal relationships Further investigation into this possibility is needed."

[0020] Akerstedt et al. (2007) reported that "Sleep and sleepiness in relation to stress and displa In "Sleep for Work Hours" (Physiology & Behavior 92), he states, "Sleep is important for preventing accidents and long-term health." "...We also look at the concept of sleep quality. However, this is dependent on sleep duration, sleep continuity, and the content of sleep stages 3 and 4. Sleep is important for people on long-term sick leave due to neurasthenia or who have high neurasthenia scores. Sleep is also significantly disrupted in people with sleep disorders, particularly increased sleep fragmentation, sleep efficiency and sleep duration. "Stages 3 and 4 (SWS - deep sleep) are reduced."

[0021] Dijk (2010) “Slow-wave sleep deficiency and enhancement: Implications for ins omnia and its management” (The World Journal of Biological Psychiatry;11(S1)) stated, "Age-related declines in SWS and SWA (slow wave activity) are well established. In some studies, anxiety, depression, and insomnia have been associated with decreased SWS and SWA. Experimental reduction of SWS through SWS blockade (by varying total sleep time or REM duration) It has been reported that sleep deprivation (not changing sleep patterns) leads to increased daytime sleepiness and decreased performance. Therefore, SWS and SWA are thought to contribute to the restorative processes occurring during sleep. "It is being done," he said.

[0022] Various methods to improve a user's sleep include physical exercise, breathing exercise, and the use of music, light, temperature, etc. This includes optimizing the user's ambient conditions for example to improve sleep: You can take the approach. 1. Caffeine can remain in your body for hours, so it's best to drink it at least before bedtime. Avoid caffeine 4 hours before. 2. Smoking (or chewing tobacco) before bedtime and / or smoking (or chewing tobacco) when waking up during the night Avoid smoking (alcoholic beverages). 3. Avoid alcohol around bedtime. Alcohol may help you fall asleep. However, it can also wake you up later in the night and disrupt REM sleep. 4. Light snacks at bedtime may help promote sleep, but avoid heavy meals. Nuts, bananas, dairy products, green leafy vegetables, eggs, and soy products contain high levels of tryptophan. Foods containing lactic acid bacteria promote good sleep. 5. Avoid strenuous exercise within approximately 2 hours of bedtime (this may be subject-dependent). (This is the case.) 6. Keep your bedroom quiet, comfortable, and at a comfortable temperature (e.g., 65°F to 75°F). tsu. 7. Minimize noise and light in the bedroom. Use light during the day. This helps regulate your body clock. Excessive light before bedtime can affect melatonin production. do. 8. Keep the bedroom primarily for sleep and sexual activity. Avoid watching TV, using a tablet or smartphone. Avoid using smartphones, listening to the radio, or eating in the bedroom.

[0023] A regular sleep schedule Generally, people should try to maintain a regular sleep schedule. If a person stayed up late on Friday and slept late on Saturday, they would sleep even later on Saturday night. This is called "Sunday night insomnia." This may cause

[0024] In practice, this means trying to wake up at the same time every day, even after a late-night party. This means you can relax, especially if you are facing "Sunday night insomnia." "Sleeping in late" on the weekend to make up for sleep debt from the week (Webster 2008) is completely effective. It also suggests that it is not without merit.

[0025] insomnia Insomnia is a chronic sleep problem (lasting at least one month) possibly accompanied by fatigue It is meant to interfere with daily activities through a persistent feeling of being overwhelmed, irritable, or simply bored. Taste.

[0026] There are four main symptoms of insomnia. Sleep problems Problems staying asleep Waking up too early in the morning (and not being able to go back to sleep) - Not feeling refreshed in the morning

[0027] Sleep deprivation can result in: ·Weakened immune system High blood pressure Increased incidence of traffic and workplace accidents

[0028] sleep-disordered breathing The term sleep-disordered breathing (SDB) refers to periods of apnea (e.g., cessation of airflow for 10 seconds or more). and hypopnea (e.g., at least 30% hypopnea for 10 seconds or more accompanied by oxygen desaturation or arousal) It can refer to a condition in which reduced airflow (SDB) is present during sleep. One in five adults suffers from SDB. It is estimated that they have (Young et al. 2002).

[0029] Wearable devices such as watches, armbands, head-mounted devices, and contactless products A variety of monitoring and sleep improvement products have been (and are) available on the market, including sleep monitoring devices. Examples of this are the following brands: Sleeptracker watch The stage is monitored overnight and the data is used to determine the exact moment when a person wakes up. (helps you feel refreshed and energized), Lark (sleep assessment and Larklife (a similar product to Lark but with a list of Jawbone Up (a wristband that measures the amount of sleep, light sleep, and Tracks deep sleep time and wake-up time), Nike Fuelband (wristband) , activity and sleep tracker), Bodymedia (armband, duration and quality of sleep) Zeo (a headband sleep management system that tracks the quality of a user's sleep patterns) It allows you to create charts and provides daily personalized assessments and expert advice. Omron Sleepdesign (wireless, health advice and customer support) (Full Sleep Health video with a scalable summary) and Gear 4 Renew Sleepclock (similar to Omron's Sleepdesign, optimized wake-up) is added). Summary of the Invention

[0030] The systems and methods of the present technology detect sleep and provide feedback to the user regarding sleep. can be provided.

[0031] Some versions of the technology optionally include any one or more of the following features: It may include a device having multiple. The device is placed at the user's bedside and is discreetly placed in the user's Recording and analyzing the sleep environment (light, sound, and temperature, as well as humidity and / or air quality) can be done. The device tracks the user's sleep, breathing, and heart rate patterns (sleep The patient's blood pressure, heart rate, and cardiac / pulmonary patterns can be monitored and analyzed. This device helps regulate the user's breathing and facilitates the user's sleep. To help users fall asleep and stay asleep by generating soothing sounds that help them fall asleep. This device can actively assist in sleep conditions. : Automatically detects and gently turns off the sound after the user falls asleep can be done. The device can chart the user's sleep patterns and provide text or Personalized recommendations can be sent via email to help users improve their sleep. These customized advice "nuggets" can help a person sleep better. and can be based on clinical research. The device provides expert advice articles and access to moderated forums. It is possible. This device communicates with the user's smartphone and performs various levels of data processing. That processing power can be used and information can be delivered to users.

[0032] An example of the present technology provides a system for sleep management that allows users to sleep significantly better. Some of the features included are sleep patterns and record the bedroom environment and provide personalized recommendations to improve the user's sleep environment and habits. Helping users with customized personal advice programs during the day and Provides personalized recommendations for evening routines to set users up for better sleep to help users relax and fall asleep more easily To provide specific recommendations to users, and to make users feel more refreshed. Waking up the user to allow for and / or the user needs further help This may include connecting the user to a resource in case of an emergency.

[0033] In some of its more specific aspects, the proposed technology measures the user's breathing rate (respiratory rate). By tracking the breathing rate (also known as inspiration rate) and guiding the user to reduce their breathing rate, This helps the user to relax. slows your breathing, helps you fall asleep faster, and recovers better from the stresses of the day. The "mind clearing" feature will wake the user up if not removed. This system helps users clear their minds of their own thoughts that may be keeping them up. The system uses bio-motion sensors to track a user's sleep, breathing, and heart rate patterns. It can record the user's physical activity (generally associated with the amount of deep sleep) Recharge levels (commonly associated with the amount of REM sleep) and mental recharge levels (commonly associated with the amount of REM sleep) This can then be viewed on a PC or a smart device such as a phone or tablet. It can be visualized by a simple numerical value or chart plot on the device screen. The system and method may use sensors such as light, sound, temperature, humidity, and / or air quality sensors. The proposed system and method measures bedroom environmental parameters, personal sleep data, trends, and User-defined data, non-specific population data, bedroom environment data, and external environment data It also delivers customized, personal advice to help users improve their sleep.

[0034] A holistic sleep management system that can help monitor and improve a user's sleep A system and method have been proposed.

[0035] Some versions of the technology may be used to diagnose, improve, treat, or eliminate sleep and / or respiratory disorders. and / or as a medical device for use in prophylaxis, and improved Having one or more of comfort, cost, effectiveness, ease of use, and ease of manufacture can be done.

[0036] Some versions of the technology may include devices that induce relaxation in the user. The device includes a speaker that plays the sound from the sound file, and a processor coupled to the speaker. The processor may include a speaker. The period of the sound file is repeatedly adjusted while the sound file is repeatedly played. The sound file can include an exhalation cue portion and an inhalation cue portion. The exhalation cue portion and the inhalation cue portion are The ratio can be fixed throughout the repeated adjustments. The ratio of inhalation cues may be about 1 to 1.4. The repeated playback and the repeated adjustment of the file are performed in a first playback period. first playing the sound file using the sound file set to a time length of thereafter, increasing the first length of time of the file to a second, longer length of time; Repeating the playback of the sound file using the second longer duration during the playback period. It can include and.

[0037] The device adjusts the duration of the sound file until the adjustment satisfies a threshold. The threshold value can be set to 1 minute. The processor may include a minimum threshold of hit repetitions. After the adjustment for the period meets the threshold, the It can further be configured to gradually decrease the volume of the sound file being played.

[0038] The device may further include a motion sensor, the processor detecting the motion sensor determining a measure of respiration using the The apparatus may further be configured to: set the duration of the audio file;

[0039] In some cases, the processor may further include a step of: Before starting the adjustment, set the duration of the sound file as a function of the respiratory measure only once. and / or the repeated adjustment of the duration of the sound file may be fixed. This may include adjusting the duration of the sound file by a predetermined amount.

[0040] Optionally, the processor is configured to determine sleep or wakefulness of the user using the motion sensor. The processor may be further configured to determine a measure of wakefulness when sleep is detected. If so, the sound file to be played through the speaker is Gradually reducing the volume and gradually reducing the volume if arousal is detected delaying the reduction of the sound played through the speaker for a second period of time; gradually reducing the volume of the sound file being played, wherein the further second period comprises: The additional first period may be further configured to be different from and reduced. do.

[0041] In some cases, each adjustment of the duration of the sound file may be performed on any of the sounds in the sound file. The pitch can be substantially maintained.

[0042] Some versions of this technology involve a processor in the device inducing relaxation in the user. The method may include using a processor to play the sound file through a speaker. and repeatedly adjusting the duration of the sound file while repeatedly playing it. The sound file may include an exhalation cue portion and an inhalation cue portion, The cue portion and the inhalation cue portion are played back repeatedly and repeatedly. The ratio of the expiratory cue to the inspiratory cue is approximately 1. The repeated playback of the sound file and the repeated The adjustment is performed by using the sound file set to a first length of time during a first playback period. First playing a sound file and then shortening a first length of said file to a second length. and increasing the time length to a longer time length during a second playback period using the second longer time length. and repeatedly playing the sound file. Repeatedly playing the audio file until the adjustment of the duration of the audio file meets a threshold. The threshold can be adjusted repeatedly with a minimum threshold of repetitions per minute. The processor may further include a step of: determining whether the adjustment of the period of the sound file satisfies the threshold; After the audio file has been played, the volume of the audio file being played through the speaker is increased for a further period of time. The processor may use a motion sensor to measure breathing. and the processor calculates the duration of the sound file as a function of the determined respiration measure. Optionally, the processor may set the expected Before starting the repeated adjustment of the duration of the sound file, the duration of the sound file is adjusted in relation to the respiratory measure. The repeating period of the sound file can be set only once as a number. Adjustment involves adjusting the duration of the sound file by fixed, predetermined increments.

[0043] In some cases, the processor may use a motion sensor to determine whether the user is asleep or awake. and if sleep is detected, the processor may determine a further first measure of Gradually reduce the volume of the sound file being played through the speaker for a period of time. and if an awakening is detected, the playback is continued through the speaker for a further second period. Gradually decrease the volume of the sound file being played or and the further second period of time may be delayed, the further second period of time being different from the further first period of time. Optionally, in some / any cases, each adjustment of the duration of the sound file is You can maintain the pitch of any sound in the sound file.

[0044] Some versions of the technology may include devices that promote sleep in users. The device may include a microphone for detecting the user's voice. a signal generated by a sensor coupled to the microphone and indicative of the user's movement; The processor may include a processor configured to receive the and further configured to analyze the received signal and detect sleep information therefrom; Upon receiving an activation signal, the system records the user's voice message and further configured to store vocal message data in a memory coupled to said processor. This allows the user to relax and promote sleep by removing their mental activity. You can record your thoughts in the same way.

[0045] In some cases, the processor may transmit the recorded audio message to the device. The processor may be further configured to play the text using a speaker. and controlling the conversion of the voice message into a text message and converting the text message into a data message. The processor may be further configured to store the data in the memory as The text message may be forwarded to a user. The transmission may include an SMS or email communication. In some cases, the activity The activation signal includes a voice activation signal, whereby the processor Using a microphone, detect the user's voice command that initiates the voice recording process. .

[0046] Some versions of the technology may include a processor method for promoting sleep in a user. The method includes using a processor to analyze signals from the motion sensor and extract from the signals: The method may include detecting sleep information from the processor. Upon receiving an activation signal, the user's voice message is transmitted by a microphone. and storing the audio message data in a memory coupled to the processor. The method may include storing the user's mental activity. This method allows for recording of thoughts to promote sleep. Using the processor, the recorded audio message is played through a speaker. The method may include, using a processor, adding the audio to a text message. Controlling the conversion of a voice message and storing the text message as data in the memory The method may include, using the processor, transmitting the The method may include initiating a forwarding of the text message. In some cases of the method, the activity may include MS or email communication. The activation signal may include a voice activation signal, whereby the processor , the user's voice command to initiate a voice recording process using the microphone. Detect.

[0047] Some versions of the technology include devices that promote sleep in users. The apparatus may include an alarm device that generates an alarm to wake the user. A program configured to prompt a user to input a wake-up time and a wake-up time window. The device may include a processor, and the wake-up time window ends at the wake-up time. The processor may be configured to receive a signal from a motion sensor, the signal being The processor of the device interprets the received signals indicative of the movement. The processor of the device may be configured to detect sleep information using sleep analysis. , the alarm as a function of the sleep information and the wake-up window and the wake-up time. and a sleep information triggering activation of a sleep device, The above function and the functions of the wake-up window and the wake-up time are used when the user This includes detecting a light sleep stage during the window.

[0048] In some cases, the function of the sleep information is at least a certain length of time or The sleep stage may further include being in a light sleep stage for a certain number of epochs. The function of sleep information may further include meeting a minimum amount of total sleep time. Alternatively, the processor is configured to randomize activation of the alarm. and further adapted to trigger activation of said alarm device using a determined probability function. The processor may be configured to detect whether the user is absent during the wake-up window. and further configured to trigger activation of said alarm device upon detecting said alarm. The processor may detect a wakefulness state of the user during the wake-up window. This can then be further configured to trigger activation of said alarm device. The alarm device may be any one of an audible alarm and a visible light alarm. Alternatively, the wake-up window and the wake-up time may be generated. The function of includes multiple comparisons of the current time with the wake-up window and the wake-up time. to ensure that the alarm is triggered within the wake-up window and by the wake-up time. It is possible.

[0049] Some versions of the technology may include a processor method for promoting sleep in a user. The method uses a processor coupled to the motion sensor, for example wirelessly, to measure the time of waking. The method may include prompting the user to input a time and a wake-up time window, The wake-up time window ends at the wake-up time. The method may include receiving a signal from a sensor, the signal being indicative of movement of the user. The method includes using the processor to detect sleep information using analysis of the signals indicative of movement. The method may include, using the processor, calculating a function of the sleep information. and activation of an alarm device as a function of the wake-up window and the wake-up time. The function of the sleep information and the wake-up window may be triggered. The function of the wake-up time and the wake-up time is determined by determining whether the user is in a light sleep state during the wake-up window. The method may include detecting that the user is in a home page.

[0050] In some cases, the function of the sleep information is The function of the sleep information may further include being in a light sleep stage. The method may further include using the processor to satisfy a total sleep time of a probability function that randomizes activation of the alarm; The processor triggers activation of the user's wake-up window. Evaluating whether detection of absence triggers activation of said alarm device The processor may detect the wakefulness state of the user during the wake-up window. The output of the alarm device can be evaluated to determine whether it triggers activation of the alarm device. The alarm device may be any one of an audible alarm and a visible light alarm. Optionally, the wake-up window and the wake-up time may be generated in plural. The function of includes multiple comparisons of the current time with the wake-up window and the wake-up time. to ensure that the alarm is triggered within the wake-up window and by the wake-up time. It is possible.

[0051] Some versions of the technology may include devices that promote sleep in users. The device accesses measurement data representing user movements detected by a motion sensor. The processor may be adapted to process the measurement data. and determining sleep factors having characteristics derived from the measurement data. The processor may calculate a sleep score based on the determined sleep factors. one or more of a recharge indicator, a mental recharge indicator, and a physical recharge indicator The apparatus may be further configured to generate an indicator of said one or more The processor may include a display for displaying an indicator of the number of The sleep score may be configured to control the display of the sleep score, The sleep factors are total sleep time, deep sleep time, REM sleep time, light sleep time, intermediate sleep time, and The time may include two or more of the time awake during sleep and the time onset of sleep. The features may include time domain statistics and / or frequency domain statistics.

[0052] Optionally, the sleep score may include an aggregate having multiple component values, each The component values are calculated using a function of the measured sleep factors and predetermined standard values for the sleep factors. The function may include a weighted variable that varies from 0 to 1, the weight being: The predetermined standard value may be multiplied. The function of the data is such that the at least one sleep factor is a total sleep time, a deep sleep time, a REM sleep time, When the measured sleep factor is one of a sleep time and a light sleep time, In some cases, the component values are determined based on at least one sleep function. The function of factors may be, for example, when the at least one sleep factor is REM sleep time. and the measured sleep factor may be an initially increasing and then decreasing function of the measured sleep factor. The function of at least one sleep factor for determining the component value may be When the sleep factor is one of the time to fall asleep and the time to wake up during sleep, The sleep factor may be a decreasing function of the sleep factor.

[0053] Optionally, the displaying of the sleep score includes displaying an aggregate sleep score. The display of the sleep score may include displaying a graphical pie chart. The graphic may include a pie chart divided into segments around its periphery. The size of each segment around the circumference is determined by a predetermined standard value of each sleep factor. The segment is a function of each measured sleep factor and the respective sleep factor. Optionally, in some cases, the total sleep The prescribed standard value for sleep duration is 40, the prescribed standard value for deep sleep duration is 20, and the prescribed standard value for REM sleep duration is 20. The standard value for sleep duration is 20, the standard value for light sleep duration is 5, and the standard value for waking up during the night is 10. The predetermined standard value for sleep is 10, and / or the predetermined standard value for sleep onset is 5.

[0054] In some cases, the processor may process detected ambient parameters, including ambient light and / or sound. accessing meters to adjust settings of said devices during at least some operation of said devices; The settings that are adjusted may further include screen brightness and / or volume. The processor may control the display of the mental recharge indicator, The mental recharge indicator is based on REM sleep time. It may include a function of the REM sleep factor and a predetermined standard value of the REM sleep factor. The function of the REM sleep factor and the predetermined standard value of the sleep factor is It can include increasing and decreasing functions between.

[0055] In some cases, the mental recharge indicator is based on measured REM sleep time. Displayed as a graphic indicator relating to REM sleep time as a percentage and the graphic indicator is proportionally adjusted according to the percentage. The processor has the appearance of a segmented battery that is filled. A display of a charging indicator can be controlled, and the body recharge indicator can be controlled by a deep sleep Optionally, the body recharge indicator may be based on a deep sleep phase. The deep sleep factor may include a function of a predetermined standard value of the deep sleep factor. The function of the deep sleep factor and the predetermined standard value of the deep sleep factor may include an increasing function of deep sleep time. The body recharge indicator may be configured to calculate the measured deep sleep time based on a predetermined standard deep sleep time. It can be displayed as a graphic indicator relating time as a percentage. and the graphic indicator is filled proportionately according to the percentage. It has the appearance of a segmented battery.

[0056] Some versions of this technology use measurements representing user movements detected by a motion sensor. and a method for promoting sleep using a processor adapted to access constant data. The method can process the measurement data and generate features derived from the measurement data. The method may further include determining a sleep factor associated with the determined sleep factor. Based on the indicator, a sleep score indicator, a mental recharge indicator, and a physical recharge indicator are generated. The method may include generating one or more indicators, including a color indicator. The method may include controlling the display of the one or more indicators.

[0057] The display may include the sleep score, and the sleep data on which the sleep score is based. The factors are total sleep time, deep sleep time, REM sleep time and light sleep time, wake-up time and and sleep onset time. Optionally, the features include two or more of time domain statistics and peripheral The sleep score may include wavenumber domain statistics. The sleep score may include an aggregate having multiple component values. Each component value can be calculated using a function of a sleep factor and a predetermined standard value of the sleep factor. The function may include a weighting variable that varies from 0 to 1, and the weighting is multiplied by the predetermined standard value. The function may include determining whether the at least one sleep factor is a total sleep time, a deep sleep time, or a REM sleep time. , and light sleep time, etc., the component value can be an increasing function. The function of at least one sleep factor that is determined is determined based on the at least one sleep factor. It can be an increasing / decreasing function when the component value is calculated, such as when the component value is REM sleep time. The function of at least one sleep factor is determined by the time to fall asleep. and the mid-wake time, the function may be a decreasing function.

[0058] The method may include displaying the sleep score, including an aggregate sleep score. The displayed sleep score may include displaying a graphical pie chart. The graphic pie chart is divided into segments around its periphery, Each segment size is attributed to a predetermined standard value of each sleep factor, and each segment is It is radially filled according to a function of the sleep factor and said predetermined standard value of said sleep factor. Optionally, in some cases, the predetermined standard value for total sleep time is 40, and for deep sleep time is 50. The prescribed standard value for REM sleep time is 20, and the prescribed standard value for light sleep time is 20. The predetermined standard value for the interval between sleep and wake-up is 5, the predetermined standard value for the interval between sleep and wake-up is 10, and / or The given standard value for sleep is 5.

[0059] The method may include a display including the mental recharge indicator, The indicator may be based on measured REM sleep time. The indicator displays the measured REM sleep factor and a predetermined standard value of the REM sleep factor. The REM sleep factor and a predetermined standard of the sleep factor can be obtained as a function. The function of the reference value is an initially increasing and then decreasing function of the measured REM sleep time. The mental recharge indicator may include a number based on the measured REM sleep time. A graphic indicator relating to near-REM sleep time as a percentage. The graphic indicator may be filled proportionally according to the percentage. The battery may optionally have the appearance of a segmented battery.

[0060] The indication may include the body recharge indicator, The data is based on the measured deep sleep time. It can be determined as a function of the sleep factor and a predetermined standard value of the deep sleep factor. The function of the deep sleep factor and the predetermined standard value of the deep sleep factor is The body recharge indicator may include a function that determines the measured deep sleep time based on a predetermined A graphic indicator relating the normal deep sleep time to the average sleep time as a percentage. The graphic indicator may be filled proportionally according to the percentage. The battery may have the appearance of a segmented battery.

[0061] Some versions of the technology may utilize one or more processors to create a sleep-promoting device. The one or more processors may include a device that detects a motion by a motion sensor. The device may be configured to access measurement data representative of the user's movements. Alternatively, a plurality of processors may process the measurement data and generate features derived from the measurement data. The one or more processes may be configured to determine a sleep factor having a The controller is configured to access detected environmental condition data from one or more environmental sensors. The one or more processors may be configured to generate a sleep hypnogram. The sleep hypnogram may be configured to display the sleep hypnogram over the course of a sleep session. The sleep hypnogram can plot sleep stages over time. at least one plotted in time correlation with the sleep stage or transition between sleep stages; The detected environmental conditions may further include a light event, a sound event, or the like. The detected environmental condition may include any one of a detected sleep event, a temperature event, and a The detected sleep disturbance may include an event corresponding to a period of waking up during sleep. The device may include the motion sensor and / or the one or more environmental sensors. the sensor(s) are coupled to the processor, e.g., wirelessly. and transmitting data representing the detected signals from the sensor(s) to the processor. can be transferred to.

[0062] Some versions of the technology may include a processor method for promoting sleep. The method may include receiving measurement data representative of a user's movement from a movement sensor. The method may further comprise processing the measurement data to generate features derived from the measurement data. The method may include determining sleep factors associated with one or more environmental sensors. The method may include accessing detected environmental condition data from the sensor. generating a sleep hypnogram, the sleep hypnogram comprising: During the sleep hypnogram, the sleep stages are plotted over time. This can include controlling the display provided.

[0063] Optionally, the method further comprises incorporating information of said detected environmental conditions in said hypnogram. The present invention can include displaying the detected sleep data in temporal association with the sleep stages. The environmental conditions may include any one of a light event, a sound event, and a temperature event. The detected environmental conditions may include an event corresponding to a detected sleep disturbance. The detected sleep disturbance may include a period of awakening without sleep. detecting the user's movements using the one or more environmental sensors; and / or The method may further include detecting the environmental condition by using a temperature sensor.

[0064] Some versions of the technology may include a device to promote sleep. The device may include a processor coupled to the display. The processor may include a motion sensor. The processor may be configured to access measurement data representative of the The measurement data is processed to obtain sleep factors having characteristics derived from the measurement data. The processor may be configured to measure daily caffeine consumption, daily including one or more of the following: alcohol consumption, daily stress level, and daily exercise amount. The processor may be further configured to prompt for input of user parameters, including: is a function of one or more determined sleep factors and the input user parameters. and further configured to display a temporal correlation of the multiple sleep sessions between one or more In some cases, the processor may and selecting said one or more input user parameters for said display. Optionally, the determined sleep time may be configured to guide the user. One of the factors may include the total sleep time of the sleep session. In that case, the processor may combine one or more determined sleep factors with the user's log. ambient sound level, ambient light level, ambient temperature level, and ambient air pollution level in the application and environmental data representing one or more ambient sleep conditions, including weather conditions. The processor may be further configured to display a temporal correlation of the sessions. and further configured to access weather data based on detecting the location of the device. In some versions, the device may sleep factors, one or more input user parameters, and the user's location. ambient sound level, ambient light level, ambient temperature level, ambient air pollution level, and the temporal sequence of the plurality of sleep sessions between one or more ambient sleep conditions, including weather conditions; It may be further configured to generate a correlation.

[0065] Some versions of the technology may include a processor method for promoting sleep. The method includes using a processor to generate measurements representing user movements detected by a motion sensor. The method may include using the processor to: processing the measurement data to generate sleep factors having characteristics derived from the measurement data; The method may include determining, using the processor, a daily caffeine intake. consumption, daily alcohol consumption, daily stress level, and daily physical activity. The method may include prompting for input of one or more user parameters. Using the processor, one or more determined sleep factors and the input user and determining the temporal correlation of multiple sleep sessions between one or more of the sleep parameters. This may include displaying it on a display.

[0066] Optionally, the method includes using said processor to Prompting the user to select user parameters for displaying the temporal correlation. One of the determined sleep factors may include a sleep session. The method may include determining a total sleep time of one or more determined sleep factors and one or more of the entered user parameters and the user's location. ambient sound level, ambient light level, ambient temperature level, ambient air pollution level, and weather the temporal correlation of multiple sleep sessions with one or more ambient sleep conditions, including The method can include generating:

[0067] Some versions of the technology may include a system for promoting sleep. The system may consist of one or more processors in a server(s), a smart device ( One or more processors, computers (e.g., mobile phones) one or more processors of the computer(s), or any of such processors The one or more processors may be any combination of one or more processors. The processor of the device receives measured sleep data representing the user's movements detected by the motion sensor. accessing the measured sleep data and processing the measured sleep data to generate features derived from the measured data; The one or more processes may be configured to determine a sleep factor having a The sensor may be configured to access measured environmental data representative of ambient sleep conditions. The one or more processors may determine a user lifestyle for each sleep session. The one or more processors may be configured to prompt for input of data. The system may be configured to evaluate the sleep factors to detect sleep problems. The system includes the measured sleep data, the calculated sleep factor data, the measurement and at least one of the inputted environmental data and the inputted user lifestyle data. transmitting at least some of the detected data and evaluating the transmitted data; to facilitate the selection of possible or most likely causes of the identified sleep problems. The system may optionally include a transmitter configured to receive the selected configured to receive one or more advice messages associated with the cause. A receiver may optionally be provided, and the advice message may be The system may further include a plurality of received advice messages. A display may optionally be provided to display the message to the user.

[0068] Optionally, the one or more advice messages are provided to the user upon continuous detection of said sleep problem. It can contain a series of advice messages that are generated continuously over time as they are issued. The measured environmental data includes detected light, detected sound, and detected temperature. The sleep factors may include one or more of the following: sleep latency, REM sleep time, and , deep sleep time, and number of sleep interruptions. Sleep problems can be caused by excessively short REM periods, excessively long REM periods, or excessively long REM periods. Fragmented state, excessively short deep sleep, excessively long deep sleep, and deep sleep The detected sleep time may include any one or more of the fragmented states. The sleep problem may be that the user's sleep contains too many interruptions. In this case, the measured environmental data and the input user lifestyle data evaluating the data and selecting one as the most likely cause of the detected sleep problem; Optionally, in the system, The generation of the device message may include triggering a push notification. In that case, the detected sleep problem associated with the received advice. The selected most probable cause is evaluated to detect sleep trends by evaluating historical sleep data. This can be further based on the fact that

[0069] In some cases, the one or more processors and / or the receiver may include a triage The system may be configured to receive data indicative of the results of the triage process. The process determines a risky sleep state by calculating a probability based on the detected sleep problem. Determining the probability may include determining the risk of sleep apnea, the risk of snoring, and calculating a probability of one or more of the risk of chronic insomnia. In some cases, the one or more processors and / or the receiver may include a sleep health Generated reports with information about the risky sleep conditions that facilitate access to specialists In some cases, the one or more processors may be further configured to receive The processor and / or the transmitter transmit data indicative of the user's location, to receive one or more advice messages based on the location data provided. Optionally, the received advice message may be further configured to: Jet lag advice may be included.

[0070] Some versions of the technology involve an electronic system using one or more processors to control sleep. The one or more processors may include a server (or multiple servers) for promoting sleep. (sometimes one), smart device(s) (e.g., mobile phone), residing on a computer or computers, or any combination of such processors The method may include measuring a motion representative of the user's motion detected by a motion sensor. The method may include processing the measurement data to: determining sleep factors having characteristics derived from the measurement data. The method includes accessing measured environmental data representative of ambient sleep conditions. The method can prompt the user to input lifestyle data for each sleep session. The method may include evaluating sleep factors to detect sleep problems. The method may include collecting the following types of data: said measurement data, The calculated sleep factor data, the measured environmental data, and the input user At least some of the lifestyle data is stored at a remote location. and transmitting the transmitted data to a sleep analysis server for evaluation of the transmitted data and possible sleep problems detected. The method may include facilitating selection of a cause or a most likely cause. one or more generated electronic advice messages associated with the selected cause; The advice message may include receiving an advice message that promotes sleep. The method further comprises receiving the received electronic advice message and receiving the advice content. The method may include displaying:

[0071] Optionally, the environmental data includes detected light, detected sound, and detected temperature. The sleep factors may include one or more of REM sleep time, deep sleep time, and sleep quality. Sleep duration, excessive sleep interruptions, excessively short REM periods, excessively short REM periods excessively short or prolonged REM periods, fragmented REM periods, excessively short deep sleep periods, One or more of the following conditions are present: excessively long deep sleep time; and fragmented deep sleep time. The measured environmental data and the input user lifestyle may be included. Evaluate the data and select one as the most likely cause of the detected sleep problem. Selecting can further include evaluating historical sleep data to detect sleep trends. do.

[0072] The method may include performing a triage process. The process calculates a probability based on the detected sleep problem to determine a risky sleep state. The determined probabilities may include risk of sleep apnea, risk of snoring, and risk of chronic insomnia. The method may include receiving a report indicating the results of the lyage process. The information about the risky sleep conditions can be used to facilitate access to health professionals. In some cases, at least one of the one or more advice messages One can be based on a detected location or a detected change in location. Optionally, the generated advice message may include jet lag advice. can be done.

[0073] Some versions of the technology may include a method for an electronic system to promote sleep. The method includes using one or more processors to measure the user's movements detected by the motion sensor. and / or a sleep flow diagram having measurement data representative of the movements of the user and / or features derived from the measurement data. The method may include accessing a factor by one or more processors. accessing measured environmental data representative of ambient sleep conditions using the The method includes using one or more processors to analyze the acquired sleep data for each sleep session. The method may include accessing the input user lifestyle data. using one or more processors to evaluate the sleep factors to detect sleep problems. The method may include using one or more processors to measure and evaluating the inputted environmental data and the inputted user lifestyle data to detect the detected This may include selecting one as the most likely cause of the sleep problem identified. The method further comprises: displaying one or more electronic advice messages associated with the selected one. The advice message may include generating an advice message to promote sleep. Includes content.

[0074] Optionally, generating the one or more advice messages comprises: Generate a continuous series of advice messages over time as successive sleep problems are detected The environmental data may include detected light, detected sound, and detected The sleep factors may include one or more of the following: sleep latency, RE, The detected sleep data includes one or more of sleep time, deep sleep time, and number of sleep interruptions. Sleep problems can be caused by excessively short REM periods, excessively long REM periods, or interrupted REM periods. Fragmented state, excessively short deep sleep, excessively long deep sleep, and deep sleep Fragmented time and excessive sleep interruptions may include any one or more of the following: The measured environmental data and the input user lifestyle data can be and selecting one as the most likely cause of the detected sleep problem. , and calculating a probability. , triggering a push notification. The method includes: It can be executed by a process on the work server.

[0075] Evaluating the measured environmental data and the input user lifestyle data Selecting one as the most likely cause of the detected sleep problem is The method may further include evaluating the historical sleep data to detect sleep trends. The method may further include performing a triage process. determining a probability based on the detected sleep problem to determine a risky sleep state. The probabilities obtained are used to estimate the risk of sleep apnea, the risk of snoring, and the risk of chronic obstructive pulmonary disease. Optionally, the probability of one or more of the risk of sleepiness may be included. The reage process facilitates access to sleep health professionals and provides information about the risky sleep conditions. The triage process may trigger the generation of a report with information related to the threshold. The method may trigger the generation of a report based on a comparison of the probability value with the calculated probability value. , the advice based on the detected location or the detected change in location. The method may include generating one or more of the messages. The generated advice message may include advice on how to

[0076] Some versions of the technology may include an electronic system to promote sleep. The system may include one or more processors. The processor may be a server(s), a smart device(s) (e.g. mobile phone), computer(s), or any such processor The one or more processors may be present in any combination of processors. Measured sleep data representing the user's movements detected by the sensors, and / or the measurements and accessing sleep factors having characteristics derived from the collected sleep data. The one or more processors may generate measured environmental information representative of ambient sleep conditions. The one or more processors may be configured to access the data. Access entered user lifestyle data collected for each sleep session; The device may be configured to assess sleep factors to detect sleep problems. The plurality of processors are configured to process the measured sleep data, the sleep factor data, the measurement and one or more of the inputted environmental data and the inputted user lifestyle data. and evaluating the number of sleep disorders to identify a possible cause or a most likely cause of the detected sleep problem. The one or more processors may be configured to select the selected and generating one or more advice messages associated with the cause of the problem. The advice message may include advice content to promote sleep. Alternatively, the one or more processors may Sending (or displaying) a message on a display device associated with said user It can be configured so that

[0077] Optionally, the generated one or more advice messages are A series of advice messages (or alerts) generated over time as successive problems are detected. In some cases, the measured The detected environmental data and the input user lifestyle data are evaluated. Selecting one as the most likely cause of your sleep problem involves calculating the probabilities. Optionally, the generation of advice messages can be promoted by the system. In some versions, the measurement may include triggering a flash notification. The detected environmental data and the input user lifestyle data are evaluated. Selecting one as the most likely cause of your sleep problems is done by comparing your historical sleep data. Further comprising evaluating to detect sleep trends.

[0078] The system includes one or more processes configured to perform a triage process. The triage process may optionally include a sleep sensor. The method may further include determining a probability based on the sleep problem to determine the risky sleep state. Seeking sleep reduces the risk of sleep apnea, snoring, and chronic insomnia. Optionally, the method may include calculating one or more of the probabilities of the triad. The Urge process facilitates access to sleep health professionals and provides information on risky sleep conditions. The triage process can trigger the generation of a report containing the information. Report generation can be triggered based on a comparison with the calculated probability value. In this case, at least one of the generated one or more advice messages is , may be based on the detected location and / or the detected change in location. In some cases, at least one generated advice message may include information about jet lag. It may include advice.

[0079] Some versions of the present technology may include a system for promoting sleep that includes a processor. The processor may be configured to associate the user's motion data with the user's motion data during a sleep session. The processor may be configured to receive measured sleep data collected by the sleep monitoring device. , processing the motion data to generate sleep factors having characteristics derived from the motion data. The processor may be configured to determine the one or more environmental sensors. The processor may be configured to measure ambient sleep conditions using a sleep frequency. and generating a sleep record for the sleep session using the sleep factors and the ambient sleep conditions. The processor may generate a pre-recorded image on a display coupled to the processor. The processor may be configured to display the sleep factors. The system can be configured to send the records to a server.

[0080] In some versions, the processor control instructions of the processor include an auto-start process. During the process, the motion data transmitted from the sensor module is evaluated to identify the detected motion. and determining whether a user is present or absent based on the quality of the detected breath, and detecting the presence of the user. and initiating a sleep session information collection process. Further control is possible.

[0081] In some versions, the processor control instructions of the processor include an auto-stop program. During the process, the motion data transmitted from the sensor module is evaluated to determine the presence or absence of the user. The sleep session information collection process determines whether the user is present or absent, and if the user's continued absence is detected, The user may further control the processor of the device to terminate the process. The detection of the sustained absence of the user includes determining the sustained absence with respect to an expected wake-up time. It is possible.

[0082] In some cases, the sensor module further comprises a receiver for receiving control commands; A processor control instruction transmits a termination command to the receiver of the sensor module. Optionally, the system may further control the processor. Detecting the location of the meter and / or device and recording at least the detected environmental parameters and determining whether or not the sleep session information collection process is performed based on the location of the device. configured to control the processor of the device to adjust a parameter. Optionally, the environmental parameters may include processor control instructions for This may include light and / or sound settings for the device. The meter may be adjusted to determine the local time at the detected location. In the system, a processor control instruction may be provided to To generate a user interface that selectively controls activation and deactivation. Some versions may be configured to control the processor of the device as follows: In the present application, the processor control instructions included are used to generate an alarm to remind the user to go to sleep. The device may be configured to control the processor of the device to generate a program. and processor control instructions for generating said alarm upon detection of time to sleep. The device may be configured to control the processor of the device to: can be the calculated optimal nap time. wherein the one or more environmental sensors include a humidity sensor, a sound sensor, a light sensor, and an air quality sensor. A quality sensor may be included.

[0083] Some versions of the technology use a processor in the device to monitor sleep sessions. The method may include a method for performing an application information collection process, the method comprising: The method may include receiving motion data transmitted from the motion data. processing the data to determine sleep factors having characteristics derived from the motion data. The method may include measuring ambient sleep conditions using one or more environmental sensors. The method may include the step of: determining a sleep state using the sleep factors and the ambient sleep conditions. The method may include creating a sleep log of the sleep session. and displaying the sleep factors on a display coupled to the processor. The method may include transmitting the sleep log to a server.

[0084] In some cases, the method includes using the processor to perform an auto-start process. The process may include: and evaluating the data to determine the presence or absence of a user based on the quality of the detection of the detected breath. and upon detecting the presence of said user, initiating a sleep session information collection process. and performing the method by

[0085] In some cases, the method includes using the processor to perform an automatic shutdown process. The process may include: Evaluating user data to determine user presence or absence and detecting sustained user absence and terminating the sleep session information collection process upon receiving the request. The detection of the sustained absence of the user may include determining whether the user is presently in a wake-up state with respect to an expected wake-up time. In some versions, determining a sustained absence can include The sensor module further comprises a receiver for receiving a control command, and the method further comprises: The method may further include transmitting a termination command to the receiver of the module.

[0086] The method includes detecting an environmental parameter and / or a location of the device; based on at least the detected parameters or the detected location of the device. and adjusting parameters of the sleep session information collection process. The parameters may include light and / or sound settings for the device. The parameters are used to determine the local time at the detected location. It is possible.

[0087] The method includes selectively activating and deactivating the one or more environmental sensors. The method may include generating a user interface for controlling the alarm. The method may include generating an alarm to remind the user to go to sleep. The sleep time can be generated by detecting the time to sleep. The time is to detect when the clock time meets the calculated optimal time to take a nap. The method may further include calculating the optimal time to take a nap. The optimal nap time may be based on processing logged wake-up times. In some cases, the one or more environmental sensors may include a humidity sensor, a sound sensor, It may include a light sensor and an air quality sensor.

[0088] Of course, some parts of the above aspects may form part of the present technology. Also, various of these sub-aspects and / or aspects may be combined in various ways. and may constitute additional aspects or sub-aspects of the present technology.

[0089] Other features of the present technology are included in the following detailed description, abstract, drawings, and claims. This will become clear from examining the information available.

[0090] Aspects of the present technology will now be described, by way of example and not by way of limitation, with reference to the accompanying drawings. In the accompanying drawings, like reference numerals refer to like elements. [Brief explanation of the drawings]

[0091] [Figure 1] FIG. 1 shows an overview of aspects of the present technology. [Figure 2]1 is an example diagram of the processing of data generated by sensors associated with an example system of the present technology. This diagram illustrates the movement of sleep data. This data is first collected from a user by various sensors in an "acquisition" stage and then processed during a "crunch" stage. During this processing, various characteristics and trends in the data are identified that may identify sleep characteristics and sleep patterns. [Figure 3] 1 is a block diagram of example physical components that may be implemented in some versions of the present technology. In one example, the system may use a bedside unit with sensors, a software mobile "App" or software running on a computer, and a server (e.g., a web-based cloud service) with a database. [Figure 3a] FIG. 4 illustrates an exemplary version of the technology of FIG. 3. [Figure 4] FIG. 1 is a block diagram of the hardware components and resulting data movement from the bedside unit to the online database. [Figure 5] A conceptual diagram of the hardware components within the bedside unit and their interaction with the PC. [Figure 6] FIG. 1 is a block diagram of one embodiment of an application for an Apple, Android, or other smart device. [Figure 7] 1 is a logical diagram of a web server / cloud and its data links with a smart device app or PC / laptop and application server. This shows a diagram of one or more web servers for (a) the user's web page, (b) the web page of the data link between either the smart device app or PC / laptop, and (c) external delivery of email / communication output. The user interface allows the user to access various screens to manage their account and view their sleep and environmental data as well as sleep advice delivered from the advice engine. [Figure 8]FIG. 1 illustrates the logical units of an application server (or a cloud implementation of this application), including an advice engine and user data management. [Figure 9] FIG. 1 illustrates one embodiment of a data layer that may comprise a main database and links to external systems (e.g., APIs for interoperating with other systems). [Figure 10a] FIG. 1 is a block diagram of one exemplary embodiment of a bedside unit. [Figure 10b] 1 is a block diagram of another exemplary embodiment of a bedside unit. In this example, a microcontroller runs a firmware program to sample data from various sensors (biomotion, light, temperature, etc.). The design can include button and light interfaces, memory to store data when an external communication link is not available, a security chip to manage data communication, and USB (Universal Serial Bus) and Bluetooth (wireless) interfaces. The USB port can be for charging only, or can be configured as a USB OTG (On-The-Go), i.e., to take on the role of a host or to operate as a regular USB device when attached to another host. [Figure 11]

[0023] Figure 1 is a block diagram of example components of the system, including an overview of one example advice delivery data path. RM20 can be understood as a "sleep processing" process. Data is acquired by sensors, crunched by the RM20 library, and then delivered to the user, including sleep scores and hypnograms. This data is forwarded to an advice engine. The advice engine can pull from the user's history, such as previous sleep history, previous advice given to the user, and prior sleep questionnaires to tailor the advice and generate the most appropriate advice for the user. The advice is then relayed to the user. One such embodiment of this delivery method is a push notification service that utilizes the smart device operating system. [Figure 12] FIG. 1 illustrates a methodology for sleep tracking using a connected accessory processing device (e.g., a motion sensor monitor and a smartphone) during a sleep session. In this example, sleep tracking is performed using a connected phone, which remains connected for the entire sleep session. Once the bedside (BeD) device initiates sleep tracking and the smart device remains connected, the RM20 library located in the smart device processes the received data in near real time. Once sleep tracking is complete, the processed data provides the user with information about their sleep and a clear breakdown of their results, such as a sleep score, hypnogram, and pie chart. [Figure 13]FIG. 1 illustrates a methodology for sleep tracking using an intermittently connected accessory processing device (e.g., a motion sensor monitor and smartphone) during a sleep session. In this example, sleep tracking begins with a connected SmD (e.g., smartphone / tablet), which disconnects and reconnects during the sleep session. The library stops processing data when it is no longer receiving real-time data streaming from the BeD. Post-processing results are generated for the user, and the user is notified. One such method of notifying the user can include an on-device notification. The SmD attempts to reconnect to the BeD. If the reconnection is successful, data streaming and processing resumes where it left off. Any data remaining on the BeD is forwarded for processing as if sleep session tracking were continuing normally, and the notification can be ignored. [Figure 14] FIG. 1 illustrates another methodology for sleep tracking using an intermittently connected accessory processing device (e.g., a sleep sensor monitor and smartphone) during a sleep session. In this example, sleep tracking begins with a connected SmD, and then the SmD disconnects, e.g., the Bluetooth connection is lost. The Bluetooth connection can be re-established on its own, but if the "Stop Sleep Tracking" button is pressed before the re-connection is established, the "app" provides the option to re-connect. If the user decides not to re-connect, the sleep session closes and the data then temporarily remains on the device. However, if the user decides to re-establish the connection, the data on the SmD can be transferred to the SmD for processing and uploading to the cloud. This is the onboarding flow. [Figure 15]1 illustrates a methodology for transferring data for sleep session management between a hardware device (a motion detector with sensors) and a smart processing device (e.g., a smartphone or computer). This example illustrates an onboarding flow. When a new sleep session is initiated, library processing is initiated, and if existing data exists, this data is transferred from the BeD to the SmD. Onboarding is required when a loss of connection between these two devices occurs. This occurs once the connection is re-established. One embodiment of this occurs when a new sleep session is initiated by a user. Library processing is initiated and data onboarding occurs. When the library is stopped, the sleep data is processed and made available to the user. This data is also uploaded to the cloud to provide available data to the user, enabling processing by the advice engine. If a new sleep session is not initiated after a connection loss, but a reconnection, e.g., a Bluetooth connection, is established, data onboarding can be performed once the reconnection is re-established. At this time, data remaining on the device can be transferred to the smart device for processing. The European Data Format (EDF), a standard file format designed for the exchange and storage of data time series, can be implemented in this process. [Figure 16]1 illustrates a methodology for deleting data for sleep session management between a hardware device (a motion detector with sensors) and a smart processing device (e.g., a smartphone or computer).

[0023] FIG. 1 illustrates an onboarding flow for different users. In the event of an unexpected disconnection, such as a Bluetooth connection loss, data may remain on the BeD. This embodiment gives the user the option to delete the data remaining on the BeD before another user connects to the BeD. If the user decides to remove the data stored locally on the BeD, they must start a library to enable near-real-time data transfer and processing. Once the library is stopped, the user can view the data following post-data processing. This data is then also uploaded to the cloud for processing by a back-end server, i.e., for advice engine processing. [Figure 17] FIG. 1 illustrates a methodology for ceasing sleep session logging, such as when the user is no longer within range of a motion sensor. Such auto-stop logic can stop logging data when the user is no longer within range. The probability of user absence / presence is determined based on characteristic breathing signals and / or gross large-scale movement. Auto-stop is a mechanism that stops the BeD from over-recording. If the user is deemed awake or absent, the SmD stops monitoring / recording and the data is processed by the RM20 library. Post-processing data is made available for user evaluation. The data is uploaded to the cloud for implementation by the advice engine. [Figure 18a]

[0023] Figure 1 illustrates a signaling pathway that can be provided by a device of the present technology when implementing real-time bio-motion / environmental signal processing and storage. In this example, temperature compensation is applied to correct for self-heating. Also shown is the operation of anti-aliasing filters and resampling. [Figure 18b]

[0023] Figure 1 illustrates a signaling pathway that can be provided by a device of the present technology when implementing real-time bio-motion / environmental signal processing and storage. In this example, temperature compensation is applied to correct for self-heating. Also shown is the operation of anti-aliasing filters and resampling. [Figure 19] 1 is a flowchart of an example sleep staging methodology that may be implemented by the processing of the devices described herein. [Figure 20] FIG. 10 illustrates the output of an exemplary sleep staging process in the form of a hypnogram or the like. [Figure 21] FIG. 10 illustrates another detailed example of a sleep staging process that may be implemented by one or more processors of a device of the present technology. [Figure 22] FIG. 1 illustrates an exemplary methodology for wake-up alarms in some versions of the present technology. [Figure 23] FIG. 10 illustrates an example of a probability function that is a continuously increasing function versus a fixed threshold; the illustrated example probability function is used in some embodiments of the described techniques. [Figure 24] FIG. 10 illustrates an example output report with example mind and body sleep indicators that may be generated in some embodiments of the present technology. [Figure 25a] FIG. 10 illustrates an example output report with an example sleep score that can be generated in some embodiments of the present technology. [Figure 25b] FIG. 10 illustrates an example output report with an example sleep score that can be generated in some embodiments of the present technology. [Figure 26] 1 is a graph showing total sleep time versus bin total sleep time. [Figure 27] 1 is a graph showing light sleep duration versus bin light sleep duration. [Figure 28] 1 is a graph showing time to sleep versus time to bin sleep onset. [Figure 29] 1 is a graph showing REM duration versus bin REM duration. [Figure 30] 1 is a graph showing deep sleep duration versus bin deep sleep duration. [Figure 31] 1 is a graph showing WASO (Wake-Up on Start of Sleep) duration versus bin WASO duration. [Figure 32] 10A-10C illustrate example output indicators that may be generated by a processing device of the present technology, such as a processor in a smartphone. [Figure 33a] FIG. 1 is an exemplary diagram of the process of guided breathing according to one implementation of the proposed technology from the user's perspective. [Figure 33b] 1 illustrates another exemplary methodology for a processor, such as sleep-inducing guided breathing, that can be implemented in a processing device of the present technology. In one example, the recorded rate is 7 BPM (br / min). Playback begins at 14 Br / min. The user's breathing rate is acquired by a bio-motion sensor, and music is initially played at a predetermined maximum BPM, but after this initial period, it is synchronized with the user's breathing. This initial acquisition period is affected by when the user stops moving, as no value is returned if the user continues to move. A new rate is played and synchronized with the user's breathing rate. If the user's breathing rate is greater than the maximum breathing rate, this embodiment initially sets it to the maximum rate. This embodiment then follows its predetermined BPM reduction path. [Figure 34] FIG. 10 illustrates an exemplary processor methodology, such as guided breathing for relaxation, that may be implemented in a processing device of the present technology. [Figure 35a] FIG. 35 illustrates respiration rate reduction that can be implemented in conjunction with the methodology of FIGS. 33 and 34. [Figure 35b] FIG. 35 illustrates respiration rate reduction that can be implemented in conjunction with the methodology of FIGS. 33 and 34. [Figure 36] FIG. 1 is a conceptual block diagram with an exemplary process of one embodiment having a bedside unit (e.g., processing unit). [Figure 37]FIG. 1 illustrates an example process for a system for generating sleep advice that utilizes one or more servers to communicate with a processing device (e.g., a smartphone) of the system of the present technology. [Figure 38] FIG. 1 illustrates an example processing methodology for generating sleep-related advice in some versions of the present technology. [Figure 39] FIG. 1 illustrates a processing methodology for generating advice over time. [Figure 40] FIG. 1 illustrates a state machine for a processing methodology to generate advice over time. [Figure 41] FIG. 1 illustrates a correlation process for correlation of detected and recorded parameters. [Figure 42] FIG. 1 illustrates an advice process that shows how collected information contributes to advice engine analysis. [Figure 43] FIG. 10 is a diagram showing the relationship between user data and advice content. [Figure 44] FIG. 1 illustrates the process of managing advice content in some versions of the technology. [Figure 45] FIG. 1 illustrates an example push engine architecture and its interaction in generating sleep advice in some versions of the present technology. [Figure 46] FIG. 1 illustrates an example data organization suitable for implementation in some embodiments of the present technology. [Figure 47] FIG. 1 illustrates the "Clear up your Mind" recording process implemented to promote sleep. [Figure 48] FIG. 1 illustrates an exemplary triage process for analyzing data that may be implemented in some versions of the present technology. [Figure 49] FIG. 1 illustrates an exemplary process of data analysis during detection of risky sleep, which may indicate a sleep problem. [Figure 50]FIG. 10 illustrates a process flow that may be implemented in some versions of the technology by a backend server or the like that performs proactive triage advice engine processing. [Figure 51] FIG. 1 illustrates a process in detecting some exemplary "sleep problems" that can be implemented by a risky sleep determination engine. [Figure 52] FIG. 1 illustrates the classification process in risky sleep detection based on multiple data inputs. [Figure 53] FIG. 1 is a block diagram of an exemplary organization of processes involved in risky sleep detection using a risky sleep engine. [Figure 54a] FIG. 10 illustrates an exemplary output report that may be generated using a processor of the present technology. [Figure 54b] FIG. 10 illustrates an exemplary output report that may be generated using a processor of the present technology. [Figure 54c] FIG. 10 illustrates an exemplary output report that may be generated using a processor of the present technology. [Figure 54d] FIG. 10 illustrates an exemplary output report that may be generated using a processor of the present technology. DETAILED DESCRIPTION OF THE INVENTION

[0092] The present technology provides methods and methods that can enable users to achieve better sleep. The system records sleep patterns and bedroom environment parameters. Further parameters such as the user's location in the form of GPS coordinates, time, season, etc. The meter can also be recorded. Using such information, the system can To help improve your environment and habits, sleep-related outputs and your daytime and evening routines The system can generate personalized recommendations for various information resources. The system monitors the user's environment and sleep patterns, which contributes to personalized recommendations. This allows the user to have a reflective mind that, if not removed, may keep the user awake. This can help eliminate activity and aid in inducing sleep. Promotes good sleep and assists in falling asleep and waking up, allowing users to It also provides a way to wake up feeling as refreshed as possible. This can be done.

[0093] The user sleep environment is designed to provide the user with optimal sleep conditions needed to achieve restful sleep. Therefore, the user's sleep environment may affect the sleep session. These measurements can be monitored for the duration of the Process (software processing functions and procedures for detecting sleep-related data from detected movement signals) The sleep library) can then collect and process the data, allowing the advice engine to function. These measurements can trigger specific sleep hygiene advice. It not only allows you to sleep better, but also creates a link between your sleep and the data captured by the environmental sensors. The system can also identify bedroom events that may cause disturbances. The ambient light sensor can be registered, recorded, or monitored at appropriate intervals and displayed. The absolute level of light (e.g., 0 lux to 100 lux) hitting the bedside device It provides a resolution of 1 lux and the ambient temperature sensor measures the temperature of the air surrounding the BeD (e.g. +5°C to +35°C) with an accuracy of 1°C and a resolution of (for example) 0.25°C. To provide.

[0094] To monitor a user's sleep environment, the system may use any one of the following: Multiple can be used. · Continuous sound, temperature, and light monitoring and / or recording during sleep sessions. Optional filter to isolate the 5 loudest sounds at night. · Annotation of environmental conditions to the hypnogram. Room environmental conditions can be linked to waking periods. Local storage of annotations for sleep session data. Whether the room temperature, light level, or sound and / or lighting is not conducive to sleep Notes.

[0095] The technology described herein, including systems and methods, provides non-pharmacological sleep aids. This technology provides a relaxation program customized to the user's breathing pattern. It features environmental (i.e., sleep area) monitoring, sleep monitoring, and "mind clearing" note features. and combined with other sleep aid features. The technology does not require mechanical contact with the user, This means that users do not need to wear wires or sensors, which may disrupt their sleep. (For example, this technology can be used without wearing a headband or placing the phone on a mattress.) (This technology also reduces the need for a sensing mattress.) Sensing mattresses still rely on direct contact with the user's body. This technology does not store information about the user, the local environment, and other data. Provide customized advice rather than generic advice based on data from multiple sources It can analyze a larger number of different types of parameters and provide insight into the user's sleep health. It becomes possible to gather a much broader picture. For example, it is possible to separate sleep disruption into seasonal / romantic factors. It can be linked to allergies based on local weather forecasts.

[0096] Therefore, this system is designed to be able to connect the wearable attachment to the user's body in a direct manner. Monitoring breathing patterns and movements using wireless sensors without requiring contact One implementation is a contactless living device that monitors the physiological parameters and movements of a user. A body movement sensor is used. The detailed operation of this sensor is described in the above-mentioned International Patent Application No. 2007 / 003444. Nos. 143535, 2008 / 057883, 2010 / 098836, and 2 This system is described in detail in issue 010 / 036700. The bag is provided to the user (or application software), followed by contactless Bioactivity monitoring (e.g., ResMed's "SleepMinder" radio frequency device) The raw sensor data of the user's breathing and / or movement is analyzed using other non-contact (e.g., passive infrared) or contact wearables (e.g., accelerometers or piezoelectric mattresses) The system may also use a microphone-based device. Additional sensors such as photodetectors and / or thermometers (e.g., thermistor(s)) It also uses sensors to detect the presence and potential effects of factors such as light, noise, and ambient temperature while the user sleeps. Apart from monitoring the bedroom environment, the system also tracks the time of day and the user's specific location. It can have knowledge of the weather conditions and be linked to geographic and seasonally adjusted weather conditions. It can ask user-targeted questions and can be used with keyboards, touch-sensitive panels, It can receive user responses via text or voice recognition software, and all collected data can be The collected information is cross-correlated with the sleep parameters and trends detected for the individual consumer. Statistical data from the general public and / or other users may also be used. .

[0097] This system can be quietly operated from the user's bedside table (when the user issues an alarm or (Except when you deliberately choose to use silence) and operate unobtrusively. The system will not recognize certain characteristics of sleep stages, such as lucid dreaming, unless the system is in "wake" mode. No light or sound will be emitted during the sleep period (unless initiated under

[0098] Exemplary contactless biosensors measure various aspects of a user's sleep, such as respiration rate and various sleep parameters. Physiological parameters can be measured and these can be processed to determine specific aspects of the user's sleep. Determining sleep stages and the amount of time the user spends in each of these stages As discussed in more detail herein, sleep staging analysis can be performed by The presence / absence of the user and the output of multi-epoch analysis were evaluated to determine the hypnogram, sleep parameters, and Generates sleep data and a sleep score. Epochs (e.g., 30-second intervals or other suitable periods) It determines whether the user is in deep, light, or REM sleep or is awake. Such data can be presented to the user. , the user's sleep score and hypnogram as discussed later. It can provide feedback to the user about their mental and physical recovery (recharge) rate. The system monitors sleep parameters and is connected to a bedside portable monitoring unit. , visualization on the screen of a personal computer or a communication device such as a smartphone Thus, it can be displayed to the user in real time or otherwise. Other parameters such as respiratory distress (apnea index or apnea-hypopnea index) may also be optionally monitored. The sleep and sleep disorder breathing (SD) data can be collected, recorded, and presented to the user. Further details regarding performing measurement B) are set forth in U.S. Patent Application Publication No. 2009 / 0203972. No. 6,299,999, filed on Dec. 1, 2002, the entire contents of which are incorporated herein by reference. (shall be part of the

[0099] Data processing is performed on the recording bedside table device before presenting the sleep data to the user. It can be implemented in the device itself or in a separate location (e.g., data storage). Performed on an offline processing device (smartphone or website) with a You can also do this.

[0100] Whether the system continues with a particular set of parameters or Will one or more of them be changed automatically or will the user be encouraged to change them? To obtain feedback on which to base the decision, the system It can also be used in modes where parameters are fed back to the system for processing. These parameters can be used to determine the nature of the sound, the tempo of a particular rhythm, the quality of the music played, etc. Volume or presence of any other sounds in the room, setting / brightness, volume level to record message In addition, users have full access to the data and can Users may review their sleep and / or environmental data through websites or other means. This can be done.

[0101] The user may also process the data to determine changes to one or more environmental parameters. You can also be prompted to change these parameters. The user may change the lighting or temperature in the room or the volume setting of a TV set or other environmental factors. For example, a user's sleep patterns may change depending on the This suggests that users may be woken up by occasional noises around 5am. In this case, the system will reduce the noise level by closing the windows or wearing earplugs. It can suggest that the user's bedroom is currently 80 degrees F, but previous data If the user indicates that they sleep better when it is cooler, the system by opening the door or turning on the air conditioning to lower the temperature to (say) 66 degrees Fahrenheit. The user may be prompted to lower the room temperature. If it takes longer to fall asleep than usual, or if the system is currently causing the user to fall asleep too quickly, If the system detects that the user is still awake and is taking a long time to do so, As discussed above in the specification, breathing relaxation techniques may be used, or the user may be It keeps the user's mind awake by recording any thoughts that may be keeping them awake. The user can be guided to feel at ease.

[0102] If the user takes too long to fall asleep (e.g., sleep onset detection is not continuous compared to the threshold), If you need more breathing exercises during your sleep (a period that starts with the start of your sleep session), It is possible to implement alarm notifications that guide users as follows. Email, "SMS" (Short Message Service) messages, or pre-defined music Create personalized alarms in the form of text messages (or push notifications, etc.) to remind you when you're sleeping It can alert users to perform specific relaxation breathing exercises within the last few hours. can.

[0103] The rationale for this is that when the user is lying in bed and in a "stressed" state, If so, customized breathing exercises or advice can be used to release tension and relax at that time. The problem is that users may find it very difficult to or observed long duration sleep latency (extremely long duration to fall asleep). In order to meet this demand, the system A series of conversations within a few hours before bedtime, characteristic of "assisted mediation," The system can recommend scheduling breathing exercises. The system accepts input of available time from the user and adjusts breathing exercises to fit the user's schedule. It is also possible to implement a program of action.

[0104] To automatically detect such needs, the system uses objective sleep measures (sleep latency, sleep duration, number of interruptions, type and duration of different sleep stages (light, deep, REM), and The perceived quality of sleep can be measured using a simple questionnaire. For example, if a user typically: For example, if you go to bed at 11 pm, it may take you 30 minutes to fall asleep and you may spend a lot of time in bed. Those who reported having interruptions and feeling stressed / "thoughts racing" In this case, the system can recommend a breathing program for 10 pm. Reminder alerts on your phone (app alerts, email, text, audio) This program can be related to the user through a The test consisted of deep breathing exercises lasting 15 minutes using biofeedback using contact sensors. This can be followed by a period of gentle music. The goal is to relax the user within a few hours and gently prepare them for sleep. As a result, the system uses the user's heart rate and heart rate to estimate the user's stress level. Heart rate variability can be monitored. A decrease in average heart rate and / or an increase in heart rate variability may indicate This can be facilitated by breathing exercises and relaxing sounds such as:

[0105] In addition to the system prompting the user to undertake a particular action, the user may also When triggered or guided by the current environmental parameters (the nature of the sound being played, the surroundings of a particular rhythm, frequency, volume of music being played, lighting settings / brightness, room temperature, etc.) The user can also select an option to set the current setting. , for example, any proposed implementation for one or more upcoming nights. You can also review and modify your settings going forward.

[0106] In summary, the system may include any one or more of the following features: do. (1) To ensure that the user's sleep is not completely interrupted, we use a non-contact bio-motion sensor to The system is able to measure / monitor and learn the user's personal sleep patterns. (2) The system uses environmental sensors to monitor light, sound, temperature, humidity, and / or air quality. The system can monitor the user's bedroom environment, such as the user's geographic location. Other relevant factors such as altitude, time of year, etc. may also be assessed. (3) Personal electronic devices such as PCs (or tablets) or smartphones processing the monitoring results of the user's bio-kinetic data and environmental parameters via the receiving device; The processed data may be at least partially or entirely transmitted remotely. In addition to uploading data to the system server, The system then uploads the data to the user's personal web page for visual It can also be configured to allow analysis and comparison with benchmarks. (4) All data measurement and recording is "opt-in" and users have the right to choose whether or not data is collected. When this happens, you can receive notifications and take control. (5) a pause feature that temporarily stops recording one or more sensor inputs (e.g., "Privacy" button). For example, a PC, a Graphic user interface on a tablet, smartphone, or other electronic device (SmD) The interface allows the user to select from a list of sensors (e.g., microphone, temperature, motion, etc.). This allows you to temporarily disable some sensors from the BeD It can also be enabled via the privacy switch above. (6) Bedside devices (BeDs) and / or SmDs provide automated self-checks. It can be run periodically, such as every evening, and can be automatically restarted if necessary, for example, when a fault is detected. It is possible to reset the (7) The system uses caffeine, alcohol, exercise, and sleep pills, as well as related Any intake, such as amount consumed, strength / brand, and time (inversely related to sleep patterns) The system is configured to prompt the user to record other data, such as further details related to quality. To avoid unnecessary burden, users can specify how many leads and how much Controls what data is requested and recorded. (8) Easily selectable "aircraft" on smart devices that can be quickly detached as needed mode. (9) Due to the sensor's distance gating capability, this system does not affect the accuracy of the measurements. It is possible to treat two people in a bed by monitoring the nearest person without disturbing the patient. It becomes possible. (10) Two separate sections (monitoring each bedmate) can be used without affecting the accuracy of the system. The lamp can be used in the bedroom. (11) The sensors in this system can be used even if the user forgets to connect them to their smart device. Even when the camera is turned off, it may be set to continue recording, for example, to store data for up to seven nights. The process of synchronizing the stored data is simple (e.g., only a simple login or other process) and reasonably fast (e.g., transfer and processing It takes 15 to 30 seconds to (12) Sensor devices (e.g., bedside devices) are part of the smart device It can have a charging port. When the device needs to keep working overnight, D, or by electronic message on the device, instructing the user to plug in their smart device. This can make the user aware of the problem. (13) The system can be used by multiple users over time and on multiple devices. A single user can use the system for a period of time. You can access the records. (14) While the sensor is active, the user can continue to use the phone as normal. (receive text and phone calls, browse the web, etc.).

[0107] System Architecture - Overview As shown in Figure 1, in one view, the system can be conceptually divided into three categories or For example, in Stage A (to relax the user), Providing sleep assistance (by guiding you towards sleep detoxification) and Recording and analyzing sleep data and providing sleep recommendations and sleep coaching in Stage C The interconnections between these stages indicate the progression of sleep data. This can be understood with reference to Figure 2. Data is first collected in various ways in the "acquisition" stage. Once collected from the user by various sensors, it is processed during the "crunch" stage. During this processing, various characteristics and trends of the data, sleep characteristics, and sleep patterns are identified. Based on these characteristics and trends, the proposed system and method Provide users with recommendations and coaching in the form of a personalized experience (see, for example, Figure 36).

[0108] At a high level, the data may be collected from one of a variety of sources, such as bio-motion sensors (e.g., radio frequency motion sensors). Or collected from multiple sensors, such as room environment sensors for light, sound, temperature, and humidity. Additionally, location-specific data can be used to predict local weather patterns online. This data can be used to examine previous user data, including population normative data. The data is input into the advice engine, which analyzes the parameters (environment, biological movement, etc.) The output generator may provide information about sleep (e.g., a sleep score) and / or or advice from advice engines, etc., as discussed in more detail herein. It is possible.

[0109] Exemplary components of the system can be discussed with reference to Figure 3. The bedside unit may include a bio-motion sensor. Some of the key sleep characteristics identified using this data are sleep quality, sleep duration, , wakefulness, light sleep, deep sleep, REM sleep, number of interruptions, breathing rate, duration of movement, and strength of movement It may include degrees.

[0110] In one example, the system includes a bedside unit containing a sensor and a computer or other smart / programmable processing devices (e.g. tablets, phones, laptops) Software that runs on a mobile "App" or software and a database and a server (e.g., a web-based cloud service) having the above. The bedside unit 3000 is located near the user when the user is in bed. Placed on a bedside table, bedside rocker, stand, or other support means This device is a device that includes bio-motion sensors and other environmental sensors (or sensors). ), as well as on a smart device 3002 (e.g., a smartphone or tablet) This includes a wired or wireless (e.g., Bluetooth) link to the app. Data processing can be split between the bedside unit and the smart device, or It utilizes the available processing power on smart devices while keeping the data payload as small as possible. It can also be centralized in smart devices to harness the power of advice engines, etc. Further processing by one or more servers, typically performed on a cloud platform. The smart device and the server 3004 may be implemented as modules. communicate via a data connection. For example, as shown in Figure 3a, the bedside unit Data from 3000 sensors can be obtained from Doppler radio frequency motion sensors. , from the bedside device to the smartphone via a wireless link (e.g. Bluetooth). The advice engine then runs. The advice can be forwarded to a cloud service that is running on the user's smart device. The content can be distributed to users via the device 3002.

[0111] System Architecture - Key Elements The system can be further discussed with reference to FIG. 4, which shows the bedside unit 300 0 (BeD) and / or logging data on smart device 3002 (SmD) and the PC / laptop, smart device, server 3004, and / or "cloud" This allows data to be transferred to computer systems such as "Internet Explorer" services. Cut.

[0112] In the example of FIG. 4, the system comprises: Bedside Unit 3000 (e.g., a stand-alone powered bedside device) Vice), a communications link from the bedside unit to a smart device or PC; Smart device 3002 applications (e.g., Apple and Android d), A communication link from a smart device or PC to the cloud, backend, consumer frontend, advice generator, advice delivery engine, A cloud service (shown as server 3004) that includes an analyzer.

[0113] An example of such a system is provided in the table below.

[0114] [Table 1]

[0115] The block diagram of Figure 4 is one example implementation of the system. In the system, the bedside unit uses its hardware and / or processor, etc. This device performs most of the user and environmental monitoring and contains memory storage. Then, the device is connected via a wired (e.g., USB) link or wireless (e.g., Bluetooth, Wi a processing device or computer (e.g., a PC or smartphone) via a Wi-Fi, NFC, or other link. The computer then communicates with the smartphone / smart device / cell phone to provide sleep advice and analysis. Applications, data storage, and other services via networks such as the Internet It communicates with a set of servers that implement the connection and The server of choice may be implemented on one or more actual hardware servers / devices. It should be noted that communication can be by wired or wireless means. The system can function using either a PC or a smart device. However, if the user has a supported smart device, In some cases, greater functionality is available. The system requires a connection to a web server. Although it can function even if not connected, any number of methods are preferred. This connection allows you to connect your computer / smart device ("SmD") to the cloud server via The cloud can transfer data to and from the server. As discussed, an advice processor can generate one or more "nuggets" of advice. The system may include a back-end server that includes a device engine.

[0116] Figures 5-10 provide further details regarding the major blocks identified in Figure 4. There are.

[0117] System Architecture - Hardware - Bedside Unit ("BeD") FIG. 5 shows one possible block diagram of the bedside unit 3000. Another conceptual diagram of the side unit 3000 is shown in FIG. 10a or 10b. The illustrated design includes sensors such as bio-motion, temperature, light, humidity, and audio. The sound sensor is a microphone, not on the bedside unit but on the smart device. Power on / off and privacy (pause logging) Such functions can be implemented using microswitches or touch switches, for example, It can be implemented by switches such as quantitative touch, but is preferably included in SmD Indicators (such as single-color LEDs, two-color LEDs, or RGB LEDs) indicate the status of the device. These indicators allow the user to avoid unwanted "light" in the bedroom. The light can be turned off during sleep periods to avoid being disturbed by "harm." The color and / or intensity changes based on the detected breathing rate / waveform of the device A full display with graphics can show the status of another version. In some cases, the bedside unit can be installed on the device. A memory can be incorporated to store data for later retrieval by the Further details of the design are provided further herein. An exemplary BeD unit is , also shown in U.S. Design Patent Application No. 29 / 490,436, filed May 9, 2014. The entire disclosure of this application is incorporated herein by reference. do.

[0118] System Architecture - Smart Device / PC / Laptop - ("SmD") Figure 6 shows the block diagram of a process (i.e., "app") on the smart device 3002 or PC. For example, Figure 6 shows (a) an Apple, Android, or other smart device. (b) an application running on one of the devices; and (c) a PC / laptop. Shows web view of data / upload of data from bedside unit The app "Business Layer" processes the sensor data received from the bedside unit. In addition, background sounds (e.g., user snoring, traffic noise, garbage trucks, cars) Audio processing, including monitoring for horns and other background noises that appear in the audio signal The app can run. Sound data can be transmitted via the app, i.e., smart Through the device's internal speaker or external speakers (e.g. Bluetooth, cable) It can be distributed via a smart device (connected via a cable, etc.). Local storage in the database caches data and provides it to the user via a data connection. for fast display of statistics, graphs and advice delivered from a web server / cloud It provides local storage and can be used when a data connection between the cloud and the app is not available. The app is used to enhance the advice delivered (e.g., The user may use GPS or other means to cross-reference with weather forecasts, pollen alerts, jet lag, etc. Location data can be collected from the following locations: Use / Get / Location Data By recording, the advice is based on the actual sunrise time at the user's location. It can be linked to the user to see if they are traveling and if they are experiencing jet lag. Or, it can provide appropriate advice for managing the user's new room environment. p can recommend dietary advice.

[0119] Optional PC application or HTML5 (or other) based website An alternative for users to view statistics, graphs, and advice about their sleep data and recommendations It is possible to provide a means for

[0120] SmDs are central components in the overall system design (although SmD devices The functionality is available in BeD with different versions of suitable displays, processors, and (It can be replaced by other components.) SmD is the following: BeD control and BeD interface, cloud interface, push notification interface, D It is responsible for SP (Digital Signal Processing) and sound acquisition. The inputs to the BeD interface include the SmD and BeD. raw biomotion data, compressed biomotion data, temperature data (e.g., Celsius), and / or light data (e.g. brightness) communication. SmD Cloud Interface The service is a data exchange between BeD and the cloud / server, including user data, processed sleep data, and (status, score, etc.), allowing the exchange of annotated advice ("nuggets"). The sound acquisition may involve inputting microphone output level samples. You can receive push notifications that may include sleep-related advice. The BeD interface controls the operation of the BeD and firmware updates that update the BeD. The cloud interface of SmD can transmit user data. data (e.g., account information), processed sleep data, raw sleep data, and advice It may output feedback, sound data, temperature data in degrees Celsius, and / or brightness light data. can.

[0121] Sounds can be recorded on the SmD throughout your sleep tracking session. Vision can incorporate the following processes: Sound content does not need to be memorized; sound events are The user can be prompted to ask permission to record. The volume can be set to 1 Hz (or other In one configuration, the signal can be sampled at a certain threshold. At the end of the night, only some sounds that are louder than a certain value can be saved. sound events (e.g., five sound events, but this number can be set by the user as a software setting) can be set to a different number of events) and delete the remaining sound events. The sound frequency is also analyzed using FFT (Fast Fourier Transform) and the following are identified: snoring, high frequency sound events, and mid frequency sound events. events, and specific components such as low frequency sound events, whether they are of short duration or Identify zero crossings, peak detection, and run-length averaging, regardless of long duration The analysis can be performed using other time domain measures such as time domain scaling.

[0122] System Architecture - Web Server / Cloud Services Figure 7 shows (a) the user's web page, (b) the smart device app or PC / laptop (c) any data link between the website and any of the websites; and (d) any email / communication Illustrates one or more web server logic processes for external distribution of output. The interface allows the user to manage the user's account, the user's sleep data and environment. Viewing data, the user's personal goals and achievements, the user's progress against their peers, In addition, various screens are accessible to view sleep advice delivered from the advice engine. This allows access to

[0123] System Architecture - Application Servers / Cloud Services / Personalized Advice vinegar FIG. 8 shows the main process executed by the application server (or its cloud implementation). Business logic processes (advice engine 3006 and user data management 3008) The advice engine 3006 uses the recorded / detected user sleep data. sleep-related messages for improving sleep based on the data, etc., are described in more detail herein. It can generate advice to:

[0124] Backend cloud software provides the user backend and advice engine. These modules can have discrete modules that include common buses. business logic and one or more databases This database has a schema for user data and a schema for advice data. Both modules are separated into two different schemas: the service level and the It can be accessible through the earphone, which can be part of the advice engine. and are discussed in more detail herein.

[0125] Cloud user backend provides data and business logic for serving SmD Communication with the SmD can be through a client-server model pattern. The backend provides client backup services, and transfers user data to multiple devices. and maintain historical data (e.g., user data and sleep data). It can be responsible for the cloud user backend via the SmD interface. The inputs are user data, processed sleep data, raw sleep data, sound data, and temperature in degrees Celsius. This may include temperature data, and / or brightness light data. The output from the SmD interface is user data and / or processed sleep data, It may include device data, etc.

[0126] System Architecture - Data Store and External Systems (Application Programming) Link to the API Figure 9 shows the main database and external systems (e.g., systems that interact with other systems). These represent the data layer with links to the server (or servers, if there are several). 3004 and / or the smart device 3002. The data used in the processes of this system are The data can be stored and organized in a

[0127] Hardware - Exemplary Embodiment - Bedside Unit (BeD) Block Diagram Returning now to the reference design of the bedside unit 3000, see Figures 10a and 10b. In the example shown in Figure 10a, a microcontroller (MC U) or other processors can receive various sensors (such as bio-motion, light, temperature, noise / sound sensors) Run the firmware program to sample data from this design. Button interface and optical interface, external communication link to smart device memory to store data when the network is unavailable, security controls to manage data communications, USB interface and / or Bluetooth ( The USB port can be used only for charging. It can also function as a USB OTG (On-The-Go) device, i.e., it can act as a host. configured to operate as a normal USB device when attached to a For this reason, this device is described in more detail throughout this specification. It is constructed using components that perform the functions described above.

[0128] BeD can be, for example, one of two states: (a) out-of-session and (b) in-session. While in the out-of-session state, the BeD can It will not respond to any remote procedure calls ("RPCs") other than connection requests. D responds to all such RPCs with a failure response. The initial state is outside the session. RPC16 (session request) is Leaving the in-session state will result in the generation and storage of appropriate notifications. A notification is generated and sent to the SmD in the connected session or later It is queued for transmission. All communication with the SmD uses a packet protocol. When BeD is in the sleep session breathing state, the LED brightness will be The brightness of the LED can be changed to reflect the light level. For example, after 5 to 30 seconds, e.g., 15 seconds, the signal can be reduced to zero. When ambient light is detected, it can be assumed that it is nighttime and the user is ready to sleep. For this reason, the user or the user's Use a much lower screen intensity to avoid disturbing the user. Similarly, it is useful to generate different volumes depending on the measured noise background. Adjustable screen brightness and / or volume settings can be used for generated sounds. can be used for all device functions, or for the "smartphone" functions discussed later in this specification. It can also be used for some device functions such as "Smart Alarm" and "Mind Clear."

[0129] The BeD also has the facility to accept firmware updates from the SmD. It can also send notifications to smart devices when relevant environmental and internal events occur. The BeD provides Bluetooth connectivity to ensure good indoor connection to the SmD. Typically, the BeD is used for signal acquisition, compression, and transmission to the SmD device. The input to the BeD processor (MCU) is ,Detected bio-motion data (four channels) from the sensor, including respiration and movement; (in some configurations) ambient temperature data, light data, sound data, control signals, and / or The BeD processor then performs further processing by the SmD, etc. for raw biokinetic data, compressed biokinetic data, and converted temperature data (e.g., and / or converted light data (e.g., luminance). can.

[0130] System Architecture - Exemplary Embodiment The advice delivery data path can be considered with reference to Figure 11. The data is acquired by the sensor of the eD device (bedside unit 3000). , transmitted to the processor of the SmD (smart device 3002). In this example, The "sleep processing" function is performed by the SmD processor. is processed by the RM20 library / its value is calculated, and the processing result (if multiple) Such output data (possibly a 3D image) is then delivered to the user by the SmD processor. The data may include a sleep score and a hypnogram, which are described in more detail herein. This data is then sent by the SmD device to a cloud service server (multiple (In some cases, the request can be forwarded to the advice engine of 3004. The sleep monitoring app is based on the user's sleep history and provides the user with the sleep monitoring app. Previous advice and any pre-sleep questionnaires that the user has completed can be retrieved. Using this data, the advice engine can tailor the most appropriate advice to the user. This advice can then be sent to the SmD, etc. , and relayed to the user. One such embodiment of this distribution method is It is a push notification service that uses the operating system.

[0131] System - Sleep Tracking Exemplary Embodiments (Sleep Session Handling, Data Download) Load "Onboarding", Reconnect) In one example of this system, an application on the SmD displays a sleep screen (graphics This screen can be used to display the SmD and BeD. If sleep tracking is occurring, optionally indicate that monitoring / recording is in progress Optionally, this screen can display real-time or near-real-time information detected by the BeD. Real-time movement and / or breathing signals can be displayed. The option activates to allow the user to start going to sleep and turn on sleep tracking. Once the user indicates a desire to activate the application, the user may initiate the activation process as discussed in more detail herein. The "Pre-Sleep Questionnaire" screen is presented to the user so that they can answer the questionnaire. Once the survey is completed, the SmD sends a request to the BeD to stream the data. Once data streaming begins, the SmD can be transmitted. Processing can begin using the RM20 process described in more detail in SmD The processor then continues to request data from the BeD throughout the night, during which time The RM20 process can function in several ways: When turned on, the lights on the BeD and SmD are turned off to minimize disturbance to the user. Instead, sleep data is sent continuously from the BeD to the SmD. and periodically throughout the night or when the user ends their sleep session. In the morning of the same day, the SmD can be transmitted in a transmission session.

[0132] A sleep record is generated after processing one sleep session following the stopping of sleep tracking. Such records can be deleted after a certain period of time (e.g., one year). To ensure that the message arrives at the server(s), the following strategies are used: can be. (1) Upload your sleep data records after they are generated. (2) If the record upload fails, the SmD background service will You can attempt to upload the recording at various intervals while the application is inactive. do. (3) If two or more records fail to upload, they will be queued. One record will be uploaded per attempt.

[0133] 12 to 16 show the management of the transfer of detected sensor information on the BeD device and the Sm This can be considered in conjunction with the transfer of sleep data to a sleep tracking device, as shown in Figure 1. The session begins while the SmD device is "connected" to the BeD device for communication purposes. Such communication connection preferably lasts for the entire sleep tracking / detection session. The RM20 library is started and the stream or raw motion data is The BeD sends the data to the SmD, and the bedside device starts tracking your sleep. If the device remains connected, the RM20 light located on the smart device The library processes the data it receives in near real time. Users can turn off tracking. When sleep tracking ends at the end of a sleep session, the processed data is provides users with information about their sleep, including sleep scores, hypnograms, and pie charts These are described more fully later in this document. Explained in detail.

[0134] As shown in Figure 13, after data streaming begins, the initiated sleep session During the download, the SmD to BeD connection may be lost ("disconnected"). Since we are no longer receiving real-time data streaming from the BeD, data processing The user's post-processing results are generated and the user is notified. One such way is to use a software popup window such as The SmD may attempt to reconnect to the BeD. If the reconnection is successful, data streaming and processing resumes where it left off. During disconnection, the BeD can continue to queue detected sensor data. Any queued data remaining on the BeD after reconnection will then be processed by the SmD. sleep session tracking can be transferred to your device for further processing, and further sleep session tracking can be performed as usual. The notification can be ignored and will continue until the session is complete.

[0135] In the transfer process in Figure 14, a disconnection event occurs, giving the user an opportunity to complete the reconnection. Therefore, starting sleep tracking with a connected SmD can be done as usual. If the SmD subsequently disconnects, e.g. the Bluetooth connection is lost, If the connection is lost, the user will be notified on the SmD. The connection can be re-established independently. However, the "Stop Sleep Tracking" button must be activated by the user before a reconnection can be established. If rebooted, the SmD application will give the user the option to reconnect. If the user decides not to reconnect, the sleep session closes and is queued. Any data that is transferred will then remain temporarily on the BeD device. If you decide to re-establish the connection, the queued data on the BeD will be processed. and can be transferred to the SmD for uploading to the cloud server.

[0136] Figure 15 shows how devices manage queued data on the BeD. It shows an "onboarding" flow process that handles such onboarding. The queueing process is necessary when a loss of connection between two devices occurs. Confirm that the entered data is present and onboarding should proceed upon connection. The BeD may provide BeD notifications that identify the SmD. Depending on whether a new sleep session is started or not, the transfer can be performed. The 0 library can manage the transfer and it will be initiated after the BeD notification. The data / queued data is also present and a new session is started. If not (i.e., the session continues), the "R onboard" sequence Such onboarding is usually done when the connection is If a new sleep session is initiated by the user, The block process begins and queued data may not be transferred. When the entered data is transferred, the data left on the device is For the exchange and storage of data time series such as European Data Format (EDF) via The library can be transferred in the standard file format for which it was designed. Once the processed sleep data is available, It can also be uploaded to a cloud server to contribute to a user data history. This data is then available for advice engine processing.

[0137] In the example in Figure 16, the onboarding process deletes queued data. You can provide the user with options to: If the device remains on the BeD due to an unexpected disconnection, a BeD notification is generated. The process is performed by different users connecting to BeD so that data from different users is not mixed. Before proceeding, the user is prompted to delete any data remaining on the BeD. If the user decides to keep the data stored on the BeD, the library will be started. This allows for near real-time data transfer and processing of queued data. A new session begins when the library is stopped. The data can be viewed following post-processing and is backed up It is also uploaded to the cloud for processing by the end server and advice engine. .

[0138] System - Sleep Tracking Exemplary Embodiment - Auto Stop / Start Implements the automatic start and stop functions of the non-contact sensor (BeD) to allow users to This ensures that the sensor does not forget to start and / or stop the relevant Record relevant sleep data periodically, while recording irrelevant data from an empty bed during the day. The auto-start feature and the auto-stop feature may both be executed. Some users may have some, some may run separately, and some may not run at all. Therefore, these users may associate button presses on their devices with the start of sleep phases. If you feel there is a behavioral benefit to stopping the There can be righteousness.

[0139] Figure 17 shows the process of controlling the automatic shutdown of a BeD device. The process stops data logging when the user is no longer within range of the BeD's sensors. The SmD processor reviews the sleep data and determines whether the total sleep time falls within a threshold. Is it greater than a value (e.g., any one of 8 hours, 9 hours, or 10 hours, etc.)? If the BeD is connected to the SmD, the SmD processor The server determines the presence or absence of the user by evaluating data from the BeD's sensors. This can be achieved by detecting characteristic respiratory signals and / or by using data from sensors. The automatic stop is based on the overall large-scale movement of the data. This is a mechanism to stop recording when the user is considered awake or absent. If so, the SmD sends a control signal to the BeD to stop the monitoring / recording process of the BeD. SmD then processes the detected data using functions from the RM20 library. The post-processed data is then available for the user to review (e.g., sleep score and / or This data is then made available for review (in the form of a hypnogram). The data is also uploaded to a cloud server for evaluation by the BeD engine. If the SmD is not connected or the SmD is not connected, it will try to reconnect. This process is shown in Figure 14. This can be considered with reference to legal theory.

[0140] Other versions of the auto-stop feature may also be implemented. Such automatic start and stop functions may be implemented automatically. It allows for motion recording and presenting the "sleep" state to the user in a plausible way. For example, extracting / determining "user absent" / "user present" status information from motion data. The analysis can form a list of absent / present labels (e.g., for each 30-second epoch). The presence / absence detection module (e.g., the process of an SmD device) can , 64-second window, 1-second step) to make a causal decision and determine whether the subject is in the field of view of the sensor. The latter can indicate whether the signal is present within the test area or whether the signal is a background noise signal. The presence / absence detection methodology uses signal power level, signal morphology, and Decisions can be made based on motion detection. The probability of user absence / presence is determined by characteristic calls. Hysteresis can be determined based on the detection of suction signals and / or gross large-scale movement. With the system, users (or, for example, pets or children) can be in the room for short periods during the day. Other versions allow you to exclude the main user's Characteristic breathing and / or heart rate patterns can be used to identify this user as another user (e.g., This signal can be distinguished from that of the person in charge.

[0141] As an example, when a user first enters a bedroom, they move in and out of range of the sensors. In addition, the user may be seen getting ready for bed or being in the vicinity of the detection range. When using sleep / wake, larger movement signatures may be captured during this time. The analysis engine looks for a good quality breathing signal with little movement as the user prepares for sleep. A high percentage indicates that the condition is present. The notion that a user is considered not awake can be determined by the level of movement (strength). This is based on the fact that a decrease in the intensity and duration of the This is also due to the increased variability of breathing patterns detected by the sleep session. This can further be considered as the start of a session or an attempt to go to sleep. As mentioned above, sleeping for more than a certain period (e.g., 10 hours, 16 hours, etc.) This is a mechanism that stops BeD from over-recording if you are asleep.

[0142] Therefore, the trigger for starting a sleep session or ending a sleep session is the following data parameter: The frequency domain (e.g., high-speed Peak power levels in the respiratory band (using Fourier transform) and frequencies within and outside the respiratory band The ratio of the wavenumber (which separates clear respiratory frequencies even in low amplitude signals) and the time domain signal detection of peaks or zero crossings in the signal (helps characterize the movement) and the changing signal (movement) The root mean square (RMS) of a time domain signal is a statistical measure of the magnitude of the The method can be based on any of the following:

[0143] These measurements are taken over overlapping or non-overlapping epochs of data (typically 30 seconds long). and post-processing to remove isolated "false" breath detections. (e.g., in the case of "true absence," some background motion or small periodic signals may be present in a particular environment.) This can increase the probability that a given epoch is classified as "present", but the probability of its surrounding epochs being If the calculated probability of "presence" is low, the epoch in question is marked as "absent." (The score can be re-scored by

[0144] For the "auto-stop" feature, the primary feature can be based on the duration of absence, and optionally Optionally, it can be based on the expected wake-up time of the user. Scan for most of the presence annotations, e.g., when the user goes to the bathroom at night or when they are having a snack. This allows you to avoid tagging an "auto-stop" event when you go to the kitchen. Cut.

[0145] Optionally, a light sensor may be used alone or in combination with the above criteria to measure the temperature of the room. Detect whether the light is turned on or off by comparing it with the user's specific habits Optionally, this can be tracked by uploading it to the cloud. The device can then use this data to These user-specific habits can be obtained, which also contributes to generating personalized advice. Optionally, the system may also include a function that allows the user to go to bed and / or wake up. Provide a "target time" relative to the time of the day to trigger the auto-start and / or auto-stop features. You can also shrink the search window.

[0146] The auto-start / auto-stop feature can be configured to ensure no data is "lost". For example: For example, if the data is displayed on the device, the user can disable automatically tagged events. It may be possible to enable it.

[0147] Hardware / Firmware - Exemplary Embodiment - Environmental Data and Biomechanical Data Collection profit 18a and 18b show the real-time bio-motion / environmental signals implemented by BeD. As shown in Figure 18a, the "notification path" provided by the processing and storage The temperature sensor produces an ambient temperature signal that can be compensated for self-heating (internal temperature). Temperature compensation is applied to correct for self-heating. This signal is combined with the optical sensor signal. can be combined and fed to a processor (e.g., a microcontroller (MCU)) The processor can also generate a notification based on this signal. The data from this signal can also be stored. Similar flow paths are also found in the biomedical This can also occur in the generation of raw motion signals from motion sensors.

[0148] Thus, temperature and light are recorded by the BeD, which then stores these data in It can record at 1Hz and downsample to 1 / 30Hz. mD is one light sample and one temperature sample per 30-second epoch of sensor motion data. The pull can be stored.

[0149] Software - Sleep Staging As mentioned above, SmD devices can use RM20 processing capabilities. The processing functions provided by the 0 module are, for example, relaxation for sleep (relax- to-sleep) function, sleep score generation function, hypnogram generation function, smart alarm function, and all features that require information processing. This allows users to assess their sleep on a nightly basis. The module may implement a sleep staging process. Evaluate data obtained from sensors (e.g., biological motion, etc.).

[0150] Some processes in the RM20 library may include: 1. Raw sensor data analysis: Raw non-contact bio-motion data is stored in the RM20 library. This data is processed and used to generate a hypnogram (e.g., a 30-second sample). The sleep parameters (sleep efficiency, total sleep time, etc.) are calculated and are used in the API code. These black box outputs can be retrieved from the library via Post-Analysis Engine (PAE) output or "end of night" output can be. 2. Provides real-time output: Raw data is incrementally sent to the RM20 library. When written to the log, (near) real-time outputs become available. These include respiratory rate, Signal quality, sleep status, and smart alarm status. These include heart rate and activity reports. It may also include a bell.

[0151] The RM20 algorithm processing is, for example, 7, which corresponds to 0.125Hz to 0.5Hz. Can be specified to detect breathing rates from 0.5 breaths per minute (bpm) to 30 breaths per minute This frequency band corresponds to realistic human breathing rates, hence the term "in-band." The term refers to this frequency range.

[0152] Before the core RM20 algorithm can be implemented, the sensor data , processed using an anti-aliasing (AA) filter, thinned to 16Hz, 16Hz This can be used for activity analysis (e.g., see Figure 18b). ) is useful for causal detection. Phase demodulation techniques are used to detect non-contact sensor signals (16 Hz). The activity is mapped to 1 Hz according to the following equation. At each epoch, additional analysis is performed. The results are then run, giving epoch-based activity counts.

[0153] Time domain statistics are calculated using 64 second overlapping data windows with 1 second step length. The calculation follows the law of causality and uses past data to enable real-time processing. Non-causal methods allow for offline processing. Sleep scores can be calculated using non-causal methods, e.g. It can be calculated at the end of the recording using phnogram methodology.

[0154] Then, for each window and each channel, the following features are calculated: mean, standard deviation, and range. Each 64-second window has 1024 (64 seconds at 16 Hz) can contain data points, so the algorithm(s) is a 512-point FFT for each (I and Q signal components) data window. These FFT results can be used to calculate the respiratory rate. Data from biomotion sensors or their signal generators can be collected at various speeds and resolutions. Typically, only one speed / Only the resolution is implemented in BeD. RF bio-motion sensor extracts motion features and respiration. Allows for feature estimation.

[0155] Analysis of motion signals for detecting respiration, movement, and sleep staging with RM20 processing For further details, see International Application PCT / US13 / 060, filed September 19, 2013. No. 652 and International Application No. PCT / US07 / 70196 filed June 1, 2007 The entire disclosures of these applications are incorporated herein by reference. and incorporated herein by reference.

[0156] The sleep staging process can now be discussed with reference to Figure 19. Sensors Data is received at 1902. Absence detection and presence detection processes are performed at 1904 and 1906. 6. Wake and absence detection processing occurs at 1908 and 1910. REM detection and wake detection processing is performed at 1912 and 1914. Then REM, waking and deep sleep processing is performed at 1916 and 1918. Next, hypnosis The gram is generated at 1920. Thus, SmD is classified into wakefulness, absence, light sleep, deep sleep, and Sleep-related data over time for sleep sessions, including sleep and REM sleep, and sleep stages can be calculated and displayed.

[0157] A suitable exemplary hypnogram with such longitudinal data is shown in the graph of FIG. The usual information obtained on a hypnogram is a) periods of deep sleep, b) periods of REM sleep. a) displaying any one or more of a) sleep period, b) light sleep period, c) wake period, and e) Absence section, f) Event annotations (e.g., detected light events, noise events, and temperature events, and / or such events may have disrupted sleep and be associated with periods of wakefulness. g) sleep score; h) level of physical and / or mental recharge; j) date and time information. The data flow is as follows: 1. Generation of analog data from the BeD bio-motion sensor; 2. 3. The data arrives in the circular buffer; 4. The S 5. Processing using the RM20 library in SmD; 6. Displaying the results on SmD. 7. Network server (advice engine) that generates hypnograms and sleep summary information 7. The advice engine Generates advice nugget(s) based on hypnogram and sleep summary information and returning it to SmD.

[0158] Therefore, the hypnogram shows that the subject's status during each period is in the deep sleep stage. Whether it is a sleep state, a light sleep state, or a REM sleep state Every 30 seconds of the recording, whether the user is in awake status or absent status The feedback report can be provided as a pseudo (multiple surrounding epochs) Real-time hypnograms and post-processed hypnograms (sleep history) Multiple (e.g., two types) of hippocampus (using the entire record as can be seen in Therefore, the hypnogram can provide: (1) the subject's whole body Activity and motion detection modules that determine whether the person is moving or lying still. (2) whether the subject is present or absent; and / or (3) a presence detection module for determining sleep / wake detection, REM detection, deep sleep detection, and / or light sleep detection. .

[0159] Sleep / wake in 1908 and post-processing of wakefulness and absence in 1910 A filter is used to update the activity count throughout the night. The wake detection threshold is set to the filter. This threshold is combined with a ramp function that is applied to the filter output. , that awakenings are more likely to occur at the beginning of the night and less likely in the first part of the night. This shows that the absence of These can be assumed to be absent at the start and end of the data recording. Absences in a section are rescored as wakefulness. Absence periods are rescored as wakefulness periods. It must be surrounded by

[0160] REM identification in 1912 and post-processing of REM and wakefulness in 1914 To identify REM sections, a threshold for REM detection was set based on the normalized respiratory rate fluctuations. This threshold can be combined with a ramp function for the threshold. The ramp function explains why REM is more likely to occur in the later part of the night. Usually, wakefulness cannot precede REM. A short wakefulness within a long REM section Sections can be removed.

[0161] Deep sleep identification in 1916 and post-mortem profiles of REM, wakefulness, and deep sleep in 1918 Process To identify deep sleep sections, the deep sleep detection threshold is set to normalized respiration rate variability. This threshold can be combined with a ramp function for the threshold. The pump function indicates that deep sleep becomes less likely to occur after a certain portion of the night. This explains the deep sleep section near the wake section at the beginning and end of the night. Determining whether deep sleep follows REM too early If so, the end of the REM section and the beginning of the deep section The first part of the score can be re-scored.

[0162] Software - Specific Embodiments - System Flow of Sleep Staging, Relaxation for Sleep Sleep Score, Smart Alarm, Advice Engine Referring to Figure 21, an example process for the RM20 function is shown. At 2101: The sensor generates a raw motion signal, which is digitized and processed at 2102. The time domain statistics and / or frequency domain statistics are processed. The time domain statistics and frequency domain statistics can be calculated from the processed signal. 103 and 2104. Range, motion, and presence information is provided, and 21 The sleep summary flag is generated in 2105 and set by the optimizer in 06. In some versions, the optimizer is set up as follows: The processes described in more detail herein can be carried out. The filtered signal is also fed to a high-pass filtering process at 2110. The resulting signal is fed into the motion and activity detection process at 2111. The wavenumber statistics are fed into a continuous breath detection process at 2112. The time domain statistics are , which is fed to the presence / absence detection process at 2114. The respiratory rate is fed to the " It can be applied to the process of "relaxing for sleep." The information is fed into a multi-epoch feature process at 2116. The sleep features are then fed into the final sleep staging process at 2118. The final sleep staging process generates a hypnogram output for the sleep summary process 2106. These epoch features provide real-time sleep staging in 2119. This real-time sleep staging process is also fed into the provides sleep information to the smart alarm process 2120, which triggers an alarm based on the sleep data. The input sleep summary information is then sent to the advice engine at 2108 and to the This can be provided to the end-of-night display process in

[0163] In summary, the RM20 library processes bio-motion sensor data in real time. This library allows you to record your sleep patterns for each night and process them at the end of the recording. Product-specific modules supporting several features allow for the estimation of quality metrics. For example, the relaxation feature for sleep is the respiration rate obtained in real time. Similarly, smart alarm processing relies on sleep staging estimation in real time. to ensure that the user is not awakened during deep sleep within the selected time window. Provides logic for

[0164] Below is a representation of the current output provided by the RM20 process: (a) 5-state (3-stage sleep) hypnogram. This shows the current status of the subject. Is it a deep sleep (N3 sleep stage) status or a light sleep (N1 sleep stage and whether you are in REM sleep (N4 stage) or REM sleep (N1 stage) 30 seconds to record whether the stage status is awake or absent Pseudo-real-time hypnogram (since it requires a small number of surrounding epochs) and post-processed hypnograms (using the entire recording or a more complete recording). A hypnogram of the type is provided. Optionally, preliminary states can be included, which This separates the light sleep stages N1 and N2 into two states. For ease of reference, the following will be evaluated: (1) an activity and motion detection module that estimates whole body motion; (2) a presence detection module that estimates presence or absence; (3) a module capable of returning overnight respiratory rate; (4) several multi-epoch features obtained from respiratory rate and activity level; (5) Sleep staging algorithm (sleep / wake, REM detection, deep sleep detection). (b) Relax: The processed respiration rate data is provided as input to the Relax feature. do. (c) Real-time sleep staging: This output and heuristic logic The goal is to wake the user up within a user-defined time window while not in deep sleep. do. (d) Sleep Score: Sleep staging to determine how well the user slept overall An informative score is provided at the end of the recording.

[0165] Most of the processing used in the RM20 algorithm module is real-time. Processing is done using causal methods, while offline post-processing is done using non-causal methods. Functions can also be real-time, requiring only historical data. and offline acausal cases that require the complete signal to be available before analysis. The various processing methods are described in detail in the following sections.

[0166] The time domain statistics for process 2103 are based on a 64-second data stream overlapping with a 1-second step. The calculation is causal and uses historical data. In that case, the following characteristics are calculated for each window and each cycle: mean, standard deviation, and / or range. It can be derived for the channel.

[0167] Frequency domain statistics in 2104 are performed using 64-second overlapping data windows with a 1-second step length. The calculation is causal and uses historical data. The process can detect a respiration rate within a certain respiration rate window. For example, , which corresponds to 7.5 breaths per minute (bpm) to 30 breaths per minute (bpm), which corresponds to 0.125 Hz to 0.5 Hz. This frequency band corresponds to a realistic human breathing rate. In the specification, the term "in-band" refers to this frequency range 0.125Hz to 0.5Hz Each 64-second window contains 1024 (64 seconds at 16 Hz) data points. Therefore, the algorithm performs a Calculate the 512-point (N / 2) FFT of As such, the in-band spectral peaks (which can then be used to determine the respiratory rate) The in-band frequency range is used to calculate the It is used to calculate the doe's respiratory rate. For HRs of 0.75Hz to 3Hz (180 beats per minute or higher), alternative frequency bands are available. The area can also be considered.

[0168] Spectral peak ratios can also be determined in 2104. Large out-of-band peaks are identified and used to calculate spectral peak ratios. It can be understood as the ratio of the largest in-band peak to the largest out-of-band peak.

[0169] The in-band fluctuation can also be calculated in 2104. The (Hz) variation quantifies the power in the frequency band from 0.125 Hz to 0.5 Hz. This is used in the presence / absence detection module.

[0170] Spectral power level in each bin, distance from adjacent peaks, and frequency of the bin By incorporating a figure of merit that combines The spectral peaks are identified in 2104. The peaks with the highest values of the figures of merit mentioned above are identified. hmm.

[0171] Activity estimation and motion detection in 2111 Phase demodulation techniques causally convert non-contact sensor signals (16 Hz) into activity at 1 Hz. At each epoch, an additional analysis is performed to A base activity count is given. One exemplary methodology is as follows.

[0172] Phase between I and Q channels Phase is the ratio of the I and Q samples to the arctangent value of a given is found by mapping it to the closest value in the matrix Initial activity analysis: Initially, the activity counter is set to zero: ActCount=0 · Check that both I and Q signals are above the noise threshold (0.015) If so: ActCount = ActCount + 8 (but > 16) (not Otherwise: ActCount = ActCount-1 (unless < 0) do not have) If ActCount(i)≧9 and ActCount(i-1)<9 If so, the i-th data point is recorded as the start of the movement. While the movement has not started, the speed is 0. Displacement Analysis (only while motion is detected, ActCount≥9): Velocity is calculated as the change in phase between successive points, i.e., the instantaneous phase delta. do. · Displacement(16Hz)=abs(velocity). Final activity analysis: · Activity (1Hz) = average displacement per second. For computational efficiency, the activity is then mapped to the closest value in a given matrix. will be In each 30-second epoch, activity scores are summed and limited to a maximum of 30.

[0173] Presence / Absence Detection in 2114 The presence / absence detection module performs causal decision making (using a 64-second window, 1-second steps). This allows for a determination of whether a subject is present in the field of view of the sensor or whether the signal is purely noise. The latter indicates that the subject is not present. The presence / absence detection algorithm uses the signal power Decisions are made based on level, signal form, and motion detection. The maximum in-band power between the I and Q signal channels is identified. is applied to the value of , and absent and present sections are identified. If it is less than 1, absence is detected and "twitches" are not detected (twitches are , identified when the range at a given second is greater than a given threshold). If so, presence is detected.

[0174] Following presence / absence detection, several post-processing steps are performed. These steps are: , at the beginning of the recording and Describe the duration of the data at the end of (i) any existing sections longer than 15 minutes (ii) Mark all epochs before the start of the first one as absent. (iii) Mark all epochs after the end of the last one as absent. The absence reported must be within a 5-minute window from the boundary of the absence detection. The previous detected motion and the next detected motion are padded.

[0175] Real-time respiratory rate estimation in 2112 This module calculates the respiratory rate vector (1 Hz) previously calculated through spectral analysis. ) to exclude values that deviate too far from the previous average, and a respiration rate of 1 / 30 Hz. Outputs a vector of The system has three main modes of operation: Initialization (Init) mode The initial "best respiratory rate" is calculated as the average of the initial respiratory rate values of the I and Q channels. Obtained. High-Speed Output Mode For each new data point, an updated average of the signal is calculated and compared with the previous average. The I or Q (in-phase or quadrature) respiration rate value closest to the previous average is used. Safe Output Mode Similar to the high-speed output mode, with the additional condition that the output is taken per sample (1 sample / sec). In each case, the algorithm determines if the new average respiration rate is within a certain band (e.g., + / - 30%). If it is within that band, the new value is assumed to be an outlier. , will be replaced by NaN (not a number). The output is due to a condition on the maximum value allowed for the current mean (currentMean). and presence and no movement for more than a certain period of time (120 seconds in one embodiment). If it is not returned continuously, the system is set to initialization mode (InitMode). The resulting respiratory rate vector was used in all further analyses and was used to calculate SmD In the app, it is used to implement the Relax feature for sleep.

[0176] Multi-epoch analysis in 2116 In this section of the algorithm, data is processed using 30-second non-overlapping epochs. can be.

[0177] Activity Counts - Causal and Non-Causal: Here, epoch-based activity counting is used. A filter with one (causal) empirically derived coefficient is used for each epoch. A final estimate of activity in the Respiratory rate variability analysis: Subtract the moving average of the respiratory rate signal (using a given window size, REM and deep The respiration rate signal is detrended by generating a sleep respiration rate variability feature. Local variability by calculating the moving standard deviation of the detrended respiratory rate signal Find the signal. Use a shorter window (half the length) to measure a section of the moving standard deviation signal. Select . Take the minimum standard deviation within each window as the final local variation of the respiratory rate.

[0178] Sleep staging in 2119 and 2118 The sleep staging module outputs the presence / absence and multi-epoch analysis modules Generates hypnograms, sleep parameters, and sleep scores for each 30-second epoch. In each case, the subject was either asleep (deep, light, or REM), awake, or absent. A block diagram of the sleep staging algorithm is shown in Figure 19. is shown in more detail.

[0179] Smart Alarm Flowchart of Logic The system assists the user in waking up during the optimal wake-up / time state, providing the most restful sleep. It can be equipped with a smart alarm that can ensure proper sleep and wake-up. , alerts when the user is awake, excited, or in light or REM sleep. In some configurations, REM sleep stages are also included in the smart alarm. This system ensures that the user is woken up regardless of their sleep state. alarm at the end of a pre-programmed time window (e.g., a specified (The alarm will ring at the optimal time within the specified wake-up window.) This alarm will ring once every It can be set to run on selected days, such as every day, or weekdays only. Select from the list provided by the application or from a file on the SmD to set an audible alarm sound. The sleep monitoring device determines to wake the user along with the audio sound generated. You can also choose to set a time window before the alarm time. The wake-up time is determined based on near-real-time sleep staging analysis by the processing library. It is possible.

[0180] The user can select the time and alarm window for the alarm to activate / trigger. The alarm window advances the alarm time. Searches for a suitable sleep stage during sleep and wakes the user when a suitable sleep stage is detected. The user can query whether an alarm is set or not. The user can inquire about the currently set alarm time. If so, you can disable the alarm.

[0181] When the user is in deep sleep during the alarm window, the system transitions the user to light sleep. Very gradually increasing the audio alarm / music to induce sleep and then wakefulness Wait for a maximum of 20 minutes (or so) before starting to pump. This may not be 20 minutes and may depend on the length of the alarm window. The system will automatically wake you up at the end of the alarm window regardless of your sleep state to ensure you are awake. Occasionally sounds an alarm.

[0182] This feature is set to a specific time in the morning and has the opportunity to snooze for another fixed period of time. Unlike traditional alarms, smart alarms are designed to wake you up at a more convenient time. Give the user the option to have the app attempt to wake them up. The system uses real-time processed data to intelligently select the time to sound the alarm. The period during which this alarm can operate is selected by the user the night before or Selected according to schedule. When the alarm window is reached, the smart alarm The alarm sounds when a sufficiently long period of light sleep or wakefulness is selected. If no window can be found, the alarm will default to firing at the end of the window. Do the following.

[0183] The optimal time is determined based on near-real-time or real-time sleep staging analysis. The RM20 library provides the logic for whether an alarm should be tripped or not. The application must know the current epoch, the epoch number of the start of the window, and The epoch number of the end of the window is passed to the RM20 library. It executes the logic internally and passes a flag to the app. This flag determines whether the alarm is activated / stopped. This indicates cancellation.

[0184] An example use of a smart alarm can be considered with reference to the following table: Sleep Consider George, a computer scientist. George goes to bed. He is working on an SmD application. Log on and set a smart alarm window to be 30 minutes long and end at 7:30am He chooses an alarm sound and then begins his sleep session.

[0185] [Table 2] *Activation means that the smart alarm logic proceeds with a probability function rather than firing. Here, activation means that the alarm is activated to immediately wake the user up. This logic is understood to mean that the Smart Alarm window has a weighted probability of waking the user towards the end of

[0186] General prerequisites for the proper functioning of a smart alarm can include: Cut. The BeD is set up and powered on. The smart alarm is Cannot be activated without the system (although the alarm can be activated by pressing the (Fail-safe trigger to wake user when the game ends) -BeD obtains sufficient biosensor signal (although alarms may occur if the alarm window is Failsafe the trigger that wakes the user at the end of the workout. (See also the state of being present or absent) -Users can set smart alarms. If you forget or do not set it up correctly, Smart alarms (whether smart alarms are set to a daily, weekly, or other recurring cycle) Do not activate (unless in a return cycle) The user activates a sleep session. If not, the smart alarm will not activate. To wake the user in the right sleep phase, the user must use the Smart Alarm widget. You should be asleep by the time the smart alarm window starts, and you should not be asleep during the smart alarm window. If the user is awake or absent, the smart alarm , defaults to immediate activation, i.e., not smarts. It's just an alarm The volume is set high enough to wake the user up. If the alarm amplitude is reduced, the Smart Alarm will override the volume setting. may not be enough to wake the user up (unless configured to do so) Alarm scheduling is set up correctly (e.g. weekdays, every If the scheduling is incorrect, the smart alarm will be activated. The item is also incorrect The alarm should ring long enough to wake the user. The alarm should not be too short. If the alarm does not turn off automatically, the alarm may not wake the user. If an alarm requires user interaction, it can run indefinitely. .

[0187] The methodology for handling the operation of smart alarms by the SMD processor is shown in Figure 22. In 2202, the processor determines whether the current time is equal to the time of the configured start time. In 2203, it is determined whether the alarm flag is set to 0 or 1. , set low to prevent the alarm from sounding. If so, the processor determines whether a user is present using motion data analysis. If no, the alarm flag is set high, thereby triggering the alarm. If present in 2206, the processor determines whether the user is awake. This is determined using motion data analysis and sleep staging information. If yes, an alarm The alarm flag is set high in 2207 to sound the alarm. If no, The processor, in 2208, determines whether the user has performed the process for at least a certain number of epochs (e.g., , 4 or more) to determine whether the subject is in a light sleep stage. If no, go to 2214. Based on the probability function in 2214, the alarm is May be set to high at 2215, or return to light sleep assessment at 2208 In 2208, the shallow If so, the total sleep time for the sleep session is determined at 2210 (e.g., If sufficient sleep is present (by comparing with a 150 minute threshold), In 2210, if the sleep is not enough, the alarm is The end of the window time is evaluated in 2211. If so, the alarm flag is set high in 2212 which sounds the alarm. If not, the alarm flag is set low at 2213 and the process continues at 220 Return to 8 or 2210.

[0188] In 2214, the value of the probability function is calculated to wake the user up at the same time every morning. A randomized time delay is provided to avoid this. The probability of an alarm trigger using the number increases over time. The threshold is set as a function of the start of the alarm window (this value is used for the duration of the recording session). (e.g., 600 epochs from the start of the night), e.g., It can be the following one. threshold = modulus(alarm window start, 10) The variables are obtained by monitoring the current epoch as follows:

number

[0189] Figure 23 shows the function at 2214. The plot on the curve is the varying probability function The horizontal line represents the fixed threshold, which is the threshold at which the alarm window begins. The value is randomized by changing from night to night. This is called flat randomization. domization), and all values of the threshold are equally likely. The variables can be linear. Smart alarms also have an equal probability of activation. Preferably, the value of this probability function is determined by the user's 2.5 hours of sleep, 4 epochs of light sleep were obtained, and the subject was confirmed not to have woken up. Other suitable minimums may also be applied.

[0190] Software - Example Embodiment - Sleep Analysis Feedback Sleep Score, Recharge Your Mind and Body It is normal to be awake about 5% of the time during the night. All stages of sleep are important. However, a balance of deep, light, and REM sleep is what makes you feel best in the morning. The system herein provides feedback on the user's sleep quality. Processing can be performed to provide the user with a sleep score, mental respiration, and other metrics. Can serve as a charging indicator and / or a body / physical recharge indicator Such feedback is generally illustrated with reference to the examples of Figures 24, 25a, and 25b. can be considered.

[0191] There are three scores: Global Recharge Score, Mental Recharge Score, and Physical Recharge Score. These can be determined by using the RM20 library process, such as SmD devices. Standard parameters on which the score can be based are The data is generated for the device engine and placed in a standard database residing on a cloud server. An extensible standard database is created for the advice engine. The database covers a wide range of populations with 120 breakdowns, including age and gender, for example. Derived from the mean and standard deviation (in percentage terms) of sleep parameters measured over a period of time These standard values can optionally include your own data. The user's score for each element can be calculated. This allows the user's measured sleep parameters to be compared to normal sleep for a person of that age and gender. This can be done by comparing the scores for each of these factors with the corresponding distributions. compare the user's sleep factors with those of the general public (normative data) For example, the sleep a user has had is most likely related to the user's age and gender. If the user has less than any other person, the user will receive a low score for sleep duration (e.g., 7 / 40).

[0192] (a) Sleep score, night's sleep, body charge, and mental charge (b) it is easy to see the connection between the two, and (c) it is visually (c) is consistent with the standard database in the advice engine; and (d) is a common standard. (e) The sleep tab is constructed to provide more detailed information on each parameter. Easily extensible to a set of buttons aligned to a large amount of data ,It is desirable to provide such feedback.

[0193] Sleep score Following a night's sleep, some feedback regarding measurements made on the user's sleep. It would be beneficial to be able to provide users with a sleep score that meets this need. In some cases, sleep scores are used to measure how well a person sleeps. The various measured sleep parameters are then used to generate a number that somehow reflects how well the patient is doing. This formula is infinite because it can be derived from an infinite number of formulas that attempt to weight the data. , because users consider going beyond the "general standard" and, in some way, this However, users may be reluctant to accept the fact that they have been given a false positive. Scores above 100 may prove puzzling and may require alternative approaches. Therefore, in some versions, the sleep score represents the quality of the user's sleep. The sleep score can be a value on a scale of 0 to 100. The sleep score can be presented as a representation of various stages. can be aggregated, with each element being associated with a measured sleep parameter. A user's score for the element is calculated, which is calculated using the user's data alone. This can be done by comparing the user's data with previous sleep data from the same user. Alternatively, this can be done by determining the sleep parameters of the user. This is done by comparing the measurements made with the normal distribution for people of that age and sex. The further a person deviates from the general norm, the more their score will fall (each Consider a range of values for the parameter, i.e., one standard deviation from the typical mean. In the case of measurements such as REM, deviations from the general standard are too little REM and too much. This can be both positive and negative, reflecting that frequent REM may be problematic. There is a possibility.

[0194] Some of the parameters are weighted more highly than others: deep sleep time, Re Parameters such as sleep duration and total sleep time are higher than the number of sleep onsets, light sleep, and awakenings. The scores are divided into six bins: Bin 1: Input Sleep, Bin2: Light sleep, Bin3: Total sleep time (Tst), Bin4: Deep sleep, Bin5 Based on a weighted sum of one or more of Bin 1: REM sleep, Bin 6: Wake-up-on-the-spot (WASO) These can be examined with reference to the graphs in Figures 26 to 31. Each of these graphs corresponds to a standard value (vertical line) that determines the specific contribution to the sleep score. 4 shows a function relating measured values to the

[0195] In this example, the sleep score may be a value out of 100 representing the quality of sleep. Six sleep factors contribute to this score, each contributing a different amount: See Table SS for the specific contribution of each factor to the overall score. It can be obtained based on public (standard) data and may be unrelated to the user's sleep data. The following values are examples that can be changed in some embodiments:

[0196] [Table 3]

[0197] The user's score for each of these factors is used to rank each sleep factor among the general public. For example, the sleep a user gets is compared to the sleep factor of the same year. If the user sleeps less than most people of their age and gender, the user is likely to be low on sleep duration. A score (e.g., 7 / 40) is obtained. In this way, the sleep score is calculated by dividing the sleep duration by the sleep score. Maximum score for deep sleep: 40 / 100, Maximum score for deep sleep: 20 / 100, Sleep: Maximum 20 / 100 for sleep score, Light sleep: Maximum 5 for sleep score / 100, Nighttime Awakening: Maximum 10 / 100 for sleep score, Falling asleep (time to fall asleep) ): A maximum of 5 / 100 can be given for sleep score.

[0198] These six factors are divided into two distinct groups: positive and negative. Reflects the behavior of the score. Positive scores start at 0 and increase up to X. For example, Your sleep duration score starts at 0, and the more sleep you get, the higher your score will increase. For sleep onset, this score starts at 5 and decreases as the duration of sleep onset increases. Few. · Correct: TST, deep sleep, REM sleep, and light sleep. Negative: WASO and sleep onset. Research has shown that too much REM can have a detrimental effect on sleep quality. For this reason, too little or too much REM sleep can lead to low REM sleep. As can be seen by the function in Figure 29, the REM score starts from 0. and increases as the amount of REM sleep increases, up to 20. This score is After that, it slowly began to decrease to reflect the negative impact of excessive REM on sleep quality. The "bins" for each sleep factor are calculated using a probability distribution.

[0199] To obtain the sleep score, we use each bin and its associated weight and the total weight (all individual The sum of the products of the weights (sum of each weight) provides the score for each sleep factor in Table SS. and mental scores can also be provided based on deep sleep and REM, respectively, in Table SS. .

[0200] As shown in FIG. 24, the sleep score can be displayed as a numerical value of SmD (this (In this case, this is the number 54.) The total time spent in the various sleep stages can also be displayed. Figures 25a and 25b show the achievable (standard) scores given the factors in Table SS. Shows the SmD display showing a breakdown of sleep scores showing achieved scores compared to This pie chart also shows the breakdown of scores. Gives the user a clear graphical breakdown of their overall sleep score. Then, each circle segment is adjusted according to the contribution in table SS. The score is divided into 5 parts from the center outwards according to the achieved score of each sleep factor. The animation is filled in radially. For example, in Figure 25a, The bright white segment representing the factor "duration of sleep" is 360 (as per Table SS). 40% of the total circumference of the degree, and the user's Based on the ratio of sleep durations, slightly more than half (22 of 40) were filled. There are.

[0201] These included an overall score and physical well-being scores expressed in a hypnogram and a radial pie chart. Before giving the user an electrical and mental recharge score, the system asks the user how they slept that night. The radial pie chart can be thought of as a morning report that notifies the user of their sleep score. A graphical breakdown such as

[0202] recharge Several versions calling for "mental recharge" and "physical recharge" and detailed sleep analysis Then, the following signal processing is performed: (a) sleep latency estimation and / or (b) REM sleep isolation. It can be executed.

[0203] The BeD bio-motion sensors discussed above measure the overall movement of a person (or animal, such as a dog, horse, or cow). It is possible to detect both body movements and chest movements due to physiological breathing. Alternative examples include infrared-based devices or accelerometer-based devices. A group of algorithms is used to analyze the time and frequency domain representations of the sensor signals. The baseline patterns in both are distinguished and the specific sleep stages (wakefulness or absence) are identified as described above. ) can provide a probability output. Using a processing block, higher frequency motion signals are separated from signals representing chest movement. do.

[0204] In case (a) - Sleep latency estimation (i.e., a measure of time to sleep) is obtained by, for example, This is used to fade out the sequence, as discussed in "Relax for Sleep." The desired output is a transition from a wakefulness state to a "state of mind" state. Detecting the transition to light sleep and calculating sleep latency (time to fall asleep) parameters Stage 1 sleep can be thought of as a transition period between wakefulness and sleep. For example, the time to sleep may be determined by the user activating the "Relax for Sleep" feature. or the user initiates a sleep session up to the time the system detects an early sleep state. The SmD processor can calculate the time required for the Some specific parameters are measured as the subject transitions from wakefulness to the twilight stage of Stage 1 sleep. The frequency, amplitude, and "burst" of higher frequency (faster) movements when moving from one page to another. It is related to the movement pattern and respiratory rate value and waveform. The combined characteristics of these can be used to classify sleep onset. The system can be adapted to subject-specific data (e.g., For example, the subject's normal baseline breathing rate and amount of movement, i.e., the time it takes for the user to fall asleep. As you move around / fidget in bed, it learns how much you move around / fidget in bed and uses that information in the estimation process. (It can be used in various ways.)

[0205] (b) Case - REM sleep isolation: Subject-specific and population-averaged respiratory rates and waveforms Using classification knowledge (morphological processing), baseline arousal state signal types can be obtained. This can be done by periodically irregular breathing rates or occasionally irregular breathing rates (information content). It can be characterized by sudden bursts of movement (i.e., during wakefulness) and sudden bursts of movement (i.e., during wakefulness). Regularity (information content reduction) is used as a secondary benchmark condition. Sleep is marked by changes in the frequency, intensity, and burstiness of movements compared to wakefulness. In addition, REM sleep is paradoxically separated by changes observed by the subject during wakefulness. This is indicated by respiratory characteristics similar to those seen in

[0206] During REM sleep, lower levels of movement flags may be observed than during wakefulness. It should also be noted that the threshold can be adapted to the analyzed subject data during the test. In some cases, the threshold is based on subject-specific historical data stored in a database. (e.g., if the subject has a high baseline respiration rate or unusual Even if the subject has poor breathing dynamics, the system can accurately detect the sleep stage of the subject. In another example, the threshold may be a population mean of respiratory dynamics. Optionally, the relative inspiratory / expiratory respiratory waveforms can be adapted based on: In the analysis block, it can be thought of as another measure of the regularity of the signal.

[0207] The REM algorithm is a method for analyzing respiratory and movement signals known as discrete wavelet analysis. Using time / frequency methodologies for signal extraction, signal epochs can be "decomposed." This can be either a replacement process or a reinforcement process, such as an approximate entropy measure. It is possible.

[0208] Where temperature measurement (either contact or contactless) is available, these measurements may be introduced into the system in an early or later integrated form to determine sleep staging. It can be strengthened.

[0209] If an audio recording is available, the system optionally Characteristic patterns of snoring, nasal congestion, coughing, or difficulty breathing in movement and breathing patterns Optionally, the sound can be detected by a microphone. This system can analyze the data in conjunction with non-contact sensors and / or body temperature measurements. The system can provide analysis of the data under analysis and trends over multiple nights. As discussed in the document, specific audio events can also be detected.

[0210] "Recharge" refers to the amount of deep sleep ("physical recharge") and REM sleep ("mental recharge") recorded during the night. The user can also correlate these sleep states with the user's age. Based on the user's comparison level with the general population standard (and the user's perceived feeling the next day) The physical recharge score and mental recharge score are linked to the user's sleep habits and are based on the user's past sleep behavior. To this end, the system uses two battery type indicators (i.e. Physical recharge (takeover) as represented by the charge levels of the (mental, mental and physical) batteries. Sleep is a state of mind that is influenced by the amount of deep sleep obtained and the amount of REM sleep obtained. The data is provided to users on a daily basis to provide an overview of the level or percentage of users who are using the service (as indicated by the Can be viewable over a week, month, or longer timescale This can be achieved by summarizing sleep data (e.g., hypnographs, pie charts, sleep scores, etc.). (represented) on a smart device (e.g., a cell phone or tablet) or PC This can be made possible by

[0211] Thus, the recharge level may be easily understood by the user during or after the user's sleep session. This can be relayed in an easy way. This includes animations that show sleep and mental recharge values. This is done through the SmD UI (user interface) using graphics. For example, as seen in FIG. 24, the mental recharge indicator 2404 The body recharge indicator 2402 indicates the percentage of body recharge. As mentioned above, the physical recharge score and mental recharge score are shown in Table 1. It can be based on deep sleep time and REM time according to the calculations described for SS.

[0212] Alternatively, in some cases, the three sleep scores can be given by: Overall sleep score (%): ((0.5×bin1 + 0.5×bin2 + 4×bin3 +2×bin4+2×bin5+bin6))*10) Mental charge score (%): (bin5) x 100 Body charge score (%): (bin4) x 100

[0213] All three scores can be bounded between [0,100]%. Bin # is the number of bins in the table. The sleep-related parameter may be any of the sleep-related parameters, such as the SS parameter. These weights (multiplication factors) can be adjusted to suit different users (e.g., by adjusting the weights). The six measurements from the user can be reweighted in a dynamic way that accounts for user behavior. Each of the sleep parameters specified is measured and the normative data of the user of that age and gender is used. For example, if a measurement is within one standard deviation of the mean, it fills that bin. If not, the distance of the measurement from the interval is calculated (which (This will give you a number between 0 and 1) and the bin will be filled with this appropriate amount. The average score is calculated as the sum of weighted bins that give a value between 0% and 100%.

[0214] Software - Specific Embodiments - Sleep Trends (Correlator) As shown in FIG. 32, the system provides feedback on sleep trends. Sleep trends can be tracked by the app or the SmD device over a period of time. The results generated by the model are overlaid with user-modifiable user-affected variables. They provide a graphical view of the data that can be viewed on a variety of devices. An example of this is a smart device / PC website. The graphs are The input can represent data that is later processed. Other data can represent data from a sleeping bed. Requires further processing before it can be input into sleep trend analysis, such as % of time in bed. It may be necessary to provide nightly sleep-related information such as caffeine consumption. Other data provided in a pre-sleep questionnaire that can guide users In response to this survey, users can choose to ask about the amount of caffeine they drink per day, their driving habits, etc. The amount of movement, stress, etc. can be input. History trend display includes sleep score, mental score / recharge, physical score / recharge, deep sleep time, Light sleep time, REM sleep time, total sleep time, time to fall asleep, bed where you slept % time in bed, total time in bed, ambient sound level, ambient light level, ambient temperature level, ambient ambient air pollution levels, number of sleep disorders, amount of caffeine consumed, amount of exercise, amount of alcohol consumed The amount of alcohol and / or stress level may be any one or more of These last four factors determine how SmD devices are designed to provide information to the user. This can be determined through a pre-sleep questionnaire that elicits sleep quality. If a sensor or other sensor or heart rate value is available or otherwise implemented, If so, information from these may be included.

[0215] All of the information used by the process is stored in a database for a period of time so that access to the information is very convenient. Moreover, the different monitored information can be stored in the memory of the SmD for a long period of time. Viewing on SmD a display showing the temporal association or temporal correlation of any two or more of them Or the processor may generate it for viewing from the web page of the cloud service, etc. The user may use a user interface generated by the processor to Any two or more of the different monitored information can be selected. Such trend plots can include, for example: Results from the app (sleep score, amount of REM sleep, etc.) are combined with user-driven variables (e.g., drinking habits, etc.). Selectable graphs with overlaid graphs (caffeine, exercise, etc.) Scalability of graphs showing variable time scales Graphs that are easy to use and read without appearing overly complex to inexperienced users Physical Design - Easy to read and make the graph as large and easy to read as possible. Efficient layout A graph of variables that matches how the advice engine uses these same variables (e.g. For example, if the advice engine uses averages from the night for light and temperature, the graph will show the averages. can be).

[0216] The plotted features of such trends allow the user to plot different variables. For example, the user interface for the correlation process is We compared alcohol consumption (from nightly questionnaires) with changes in REM sleep over time. The user may be presented with the option to choose to The phases given by the system for REM sleep are given for ease of reference. All advice received can then be displayed to the user. found that reducing or eliminating alcohol consumption was associated with increased REM sleep duration. Users can learn more about the impact of alcohol consumption on REM sleep quality. You can also see that they are giving all the right advice (such nuggets are (If provided to the user with the content). Similarly, different amounts of caffeine consumption Allows users to visualize changes that may occur to sleep information over time. Additionally, daily caffeine consumption was compared with daily sleep information (e.g., total sleep time and / or deep sleep). It is possible to plot the time correlation between the time and the time.

[0217] Software - Example Embodiment - Relax for Sleep Some versions of this technology may include a "relaxation for sleep" process. Generally, the user's breathing rate BR is measured by a bio-motion sensor in the device (e.g., BeD). Music or other sounds can be played at a predetermined maximum rate (breaths per minute (BP)). M), i.e., the time length of the sound file. is set to match the desired breathing time length. After an initial period of time in which the music is acquired, the music can be aligned with the user's measured breathing rate. The new / adjusted BPM of the music will be adjusted to the user's breathing rate when played. If the user's breathing rate is greater than the maximum breathing rate, the music will be set to the maximum rate first. In some cases, the BPM of the music can follow a predetermined reduction path.

[0218] Background - Entrained reduction of breathing rate to induce sleep in users As noted above, one aspect of the proposed system and method is to generate a calming sound. This provides a relaxation technique that helps users fall asleep. The rhythm and volume are designed to help users vary their breathing rhythm. It can be user-selected or automatically adjusted (i.e., based on the user's breathing pattern). (a customized relaxation program for each turn). This is activated by the user. The "Relax for Sleep" feature is enabled / selected.

[0219] The premise is that a pleasant, periodic sound acts like a metronome, adjusting the user's breathing rate accordingly. Such a process tends to synchronize with the acoustic rate. See Figure 33a. Contactless sensors can measure both respiration rate and wakefulness / sleep status. This sensor feedback can provide real-time feedback on how the The clock can be used to control the gradual slowing down of the periodic rate of the sound. If the subject's breathing rate is "acquired," slowing the breathing rate can help to relax the subject. It can relax you and help you fall asleep faster. If it detects you are not awake, You can turn off the audio volume. The volume will gradually decrease to 0 instead of suddenly. This can also be reduced because sudden changes in the audio environment can cause the subject to re-enter the Because it may awaken you.

[0220] Relaxation features for sleep can use spot or continuous breathing analysis. For example, the RM20 process can be started once at the beginning of the relaxation process for sleep. Access the absorption decision function (algorithm) and select the sedative sound (SmD) selected by the user. Choose from several sound files provided by the app or from your music library This allows for easy selection of the starting repetition rate of the , tracking breathing patterns (as described below) and adjusting the sound file to only include the onset of this feature. The sound can then be adjusted according to a set pattern. The user is guided to naturally synchronize their breathing with the sound patterns. This means that the user is not limited to a specific speed at which they can relax. This differs from the meditation feature, which can guide your breathing more actively. It requires conscious engagement and thus keeps the user awake.

[0221] First "get" the breathing rate and adjust it (audio playback speed) to the initial value. After the setting, the respiration rate is not tracked and the system will find the lowest lower value ( It is possible to reduce the frequency of adjustments (audio BPM) to the BPM of the audio. is then reduced in steps to reach the desired lower value (e.g., 6 breaths per minute). This reduction allows the user to reduce their breathing rate, thus This helps you enter a more relaxed state and fall asleep more easily. The function allows the user to integrate their breathing rate into a specific playback rate over time. When the system detects that the user is not awake, As an option, consider a gradual shutdown rather than a sudden silence to avoid waking the user. The method reduces the volume of the sound to zero.

[0222] An implementation of such a process may include the following. (1) Selectable high-quality sound files (e.g., file type is AAC). (2) Option to download additional sound files. (3) A user interface for selecting and playing various sound files. (4) This feature completes the volume control or the user stops the volume control. If the user interacts with the app during a session, the volume control reverts to its default value. If you select this option, the volume will be reset to its default value. (5) Audio distribution to speakers (external if connected or integrated). (6) Real-time respiratory measures. (7) Maximum regeneration time (e.g., 60 min from the time the minimum respiration rate was reached).

[0223] In a specific example, the maximum modulation frequency may be 14 BPM. The playback speed function follows a stepwise reduction (in BPM) from 14 to 12, 10, 8, 6 However, this process may require additional information, such as respiration rate detection. If it returns a BPM value, this will change the measured frequency and then scale it in steps of 2 BPM. The step reduction is resumed. In the case discussed, this results in the following change (in BPM): That is, from 14 to 11.5 to 9.5 to 7.5 to 6. A jump or step from a minimum speed (e.g., 6 BPM) to 2 BPM in the previous example The maximum speed can be set to, for example, 14 BPM. If the user is detected breathing at a rate faster than 14 BPM, the program The process increases the playback speed of the sound samples to match the user's breathing rate. It cannot be played back at the same time, but rather maintains the playback speed at a predetermined maximum (for example, 14 BPM). From here, the speed reduction function can begin. The minimum speed reduction can be 6 BPM. If the user is determined to be breathing slower than a predetermined minimum rate, the process , playback can be started at a predetermined minimum speed (e.g., 6 BPM). , which can be directly linked to the duration of playback (e.g., 10 minutes) at a minimum speed of 6 BPM (all That is, additional time (e.g., 2 minutes) may be added at this minimum speed. The time is variable but can be, for example, approximately 60 minutes. The determination of the respiration rate may depend on whether and when the system detects the respiration rate.

[0224] The above exemplary process can be discussed with reference to the methodology of Figure 33b. In this case, the user selects the relaxing action for sleep of SmD, and music / The sound playback process is started. First, a sound file is played at, say, 14 repetitions per minute. By playing, the sound file will be played repeatedly at the initial speed (e.g., 14 BPM). In 3302, breathing is measured while the sound file is playing. The detected speed is evaluated to ensure that a valid speed has been detected. If the tempo is within a certain range (e.g., 14 BPM to 6 BPM), it will match the detected tempo. The duration of the sound file is adjusted so that it can be played repeatedly. The pitch of the sounds in the sound file is substantially maintained so that the sound sounds natural. The sound may be further processed to ensure that the detected user velocity is If disabled, the initial music BPM will be maintained if the detected speed is below the above range. The duration of the sound in the file can be played at a speed that matches the minimum value of the range above. In 3304, the sound file is played back for the period determined in 3303. In 3305, while the sound file is being played repeatedly, a timer of 2 minutes is generated. In 3306, the playback speed of the current sound file is checked and the sound is It is determined whether the current rate of the file is greater than the minimum rate. If it is greater than 3307, the duration of the sound file is increased while maintaining the pitch. By doing this, the speed is reduced by the step amount (e.g., 2 BPM). The reduction has a lower limit (e.g., 6 BMP) that is a minimum value from the range above. Repeated playback of the file then returns to 3304. At 3306, When the speed reaches its minimum value in the range, the playback of the sound file is stopped at 3308. In 3309, sleep information from sensor analysis is collected and stored for a period of time (e.g., 10 minutes). The information is evaluated to determine if the user is not awake or if the maximum playback time has been reached. If not, further review is required in 3310 before rechecking in 3309. The period (e.g., 5 minutes) continues. If the user falls asleep or reaches the maximum time, 331 At 1, the volume reduction process begins. The volume is reduced to zero or off. For example, the amount of charge is increased by a predetermined rate (e.g., 10 minutes) over some interval (e.g., 1 It can be gradually reduced by 0%.

[0225] In another example, the sequence may follow these steps: a. The user selects the Relax option. b. While waiting for the RM20 algorithm to return a valid breathing rate, audio , played at a default breathing rate of 14 breaths per minute (the maximum available). c. The maximum time the SmD App will wait for the RM20 algorithm to return a valid value is 4 minutes. Therefore, if i) the algorithm returns a valid value within this time, or i i) There are two possibilities if the algorithm does not return a valid value within this time: In the latter case, steps 4 to 10 (below) are executed in order. If the program does not return a valid value, step 4 is skipped and only steps 5 to 10 are executed. do. d. When the algorithm returns the user's breathing rate, jump to that breathing rate (which This allows the user to hear the detected breathing rate / simple feedback ). Stay at this speed for 2 minutes. e. Decrease the playback rate by 2 breaths every 2 minutes until a minimum rate of 6 breaths per minute is reached. Decrease. f. Once the minimum respiration rate is reached, remain at this rate for 10 minutes. g. After this 10 minute period ends, every 5 minutes check whether the user is awake or not. If the user is not considered awake at any of the five-minute check points, If this occurs, the volume will be reduced by 10% every minute for 10 minutes. h. To facilitate a playback time of nearly 60 minutes, the user must still be awake after a 50-minute period. If so, reduce the volume by 10% every minute for 10 minutes. i. When done, close the feature (and return to the sleep screen if in night mode) (This is the case.)

[0226] As mentioned above, the pitch of the sound file must be adjusted every time a change in the sound file speed is required. The duration of the sound file (length of time) can be increased (to slow down, to increase speed) while maintaining the By playing the sound file repeatedly, the sound file The file has the desired speed. Changes to the sound file are overwritten to achieve the duration change. This can be implemented by a stretcher function that can expand or compress the audio file length. The term "stretcher" refers to the process of stretching a source file so that it plays back slowly. locked (stretched or lengthened) or faster playback Depending on whether the file is being compressed or shortened, both stretching and compression can occur. It is used to represent.

[0227] For example, the original sound file was recorded to suit a playback speed of 7 BPM. The sound files can be a variety of sounds from nature, such as beach sounds and instrumental recordings. A variety of soothing sounds can be provided. The ratio of the air cue (the intake part of the sound file) to the air cue is set to a fixed ratio (preferred) for all files. This ratio is adjusted depending on the duration of the sound file. This ratio provides more natural guidance. This was determined through experiments using actual subjects.

[0228] The expansion process library maintains the same pitch as the original audio file. The algorithm implemented to time-stretch an audio file includes: The time expansion algorithm is implemented using the commercially available DIRAC system or other digital signal processing. In this regard, this example is a While the audio file playback speed is changed, the sound file This is a time stretching technique that allows the breathing rate to be adjusted or reduced to match the user's breathing rate. The user's breathing rate can then be reduced and synchronized. Keep your audio files sounding natural.

[0229] The stretcher process runs in real time in SmD applications. This stretcher process is applied to all audio files to create the desired speed. You can stretch or compress the time of an audio file so that it plays at the original speed. The 7 BPM speed of the Null sound file is passed to the library with a stretch value of 1 (software This can be maintained by setting the file parameters. This means that the 7 BPM file will stay at this "unchanged" speed. To change the sound, a stretch value other than 1 can be supplied to the library.

[0230] Other embodiments Various versions that play sounds to synchronize the user with relaxing breathing for sleep Any of the following features may be individually implemented in the system and method: Or they can be included in combination. (1) Reproduce a predetermined sound by encouraging the user to adjust their breathing rate / rhythm. To guide the user and ease their transition to sleep. (2) Slowing the user's breathing rhythm (breathing rhythm) to better assist the user in transitioning to sleep Soothing sounds (selected from a range of sounds according to the user's personal preference) that aid in the modulation of breathing. Tracking the user's breathing patterns while generating a Parameters such as the quality of the breathing (temperature), volume, rhythm, etc. are calculated according to the detected breathing pattern. For white noise type sound files, this file will You can set it to be on or off. and / or single frequency sounds with varying volumes. Such changes in sensory output (such as the rhythm of a particular sound or the color of a light) may affect the user's breathing rate. "Synchronize" the frequencies of the changing colors, rhythms, volumes, etc. The purpose is to make (3) The range and volume of the sound should be such that it drowns out other noises and calms the user's mental state. The input is based on the detected ambient noise level of the room environment. can be provided. (4) Because sound preferences are highly personal, users may experience a variety of listening experiences over multiple nights with the system. Get practical and helpful suggestions to help you choose the best sound based on your preferences. For example, SmD can determine which sound files induce sleep faster (e.g., on average). It is possible to detect when a signal is generated and notify the user. (5) using one or more sensors, preferably wireless, to measure the user's breathing rate and / or Other physiological parameters can be monitored. These sensors are provided to the user. The system provides feedback to the controller that drives the audio and / or optical inputs. The system detects when the user begins to fall asleep and adjusts the audio pattern. The sound is automatically switched on when the user falls asleep. fades off to

[0231] "Relax for Sleep" to end your session / The sound in the "Breathe for Sleep" function can be switched off. If it detects that the user is not awake, it can reduce the volume of the sound to zero. The notion that the user is not considered awake is in some versions Based on the detected reduction in breathing levels (both intensity and duration) and normalization of breathing and / or based on a process such as that discussed with respect to the RM20 library. This example test involves determining a trigger to initiate volume reduction over a 10 minute period. After these 10 minutes, the sound is simply turned off in a way that does not wake the user up. This allows for a gradual shutdown rather than an abrupt silence.

[0232] "Assisted Meditation" Process (Daytime Relaxation Process) Turning off the sound in your sleep mode (also known as sleep mode) can help you complete your nightly sleep routine. May differ from the "relaxation for sleep" process it is intended to assist It is important to note that, for example, one difference is that the relaxation process It is possible to not detect that the user is asleep before starting a process. For relaxation to sleep, the system evaluates the user's breathing and movement levels every five minutes. These values can then be determined as awake or not awake. Volume reduction occurs at these 5 minute checkpoints when the user is not considered awake. After the minimum target respiratory rate is achieved, When the user's breathing rate remains at that level for 10 minutes, e.g., by: , sound reduction can be implemented.

[0233] At the end of this 10 minute period, the user is checked every 5 minutes to see if they are awake. If the user is not considered awake at any of the 5-minute check points, the 10-minute check point Decreases the volume of the sound by 10% every minute for the duration of the

[0234] It then returns to the feature that initiates the adjustment to the user's breathing rate / rhythm. The breathing pattern of an overwhelmed or stressed person is focused on the upper rib cage and the abdominal muscles rather than the abdominal muscles. This is associated with the neck muscles being used for breathing, which may be shallow and rapid. Conventional respiratory biofeedback involves measuring the respiratory rate using sensor belts on the chest and abdomen. , the breathing pattern can be visualized on a computer screen, thus allowing the user The system of this technology allows the user to slow down their breathing rate and focus on deep breathing. can be correlated with the user's respiratory parameters using the display on the SmD, but are not identical. The present invention provides a method for generating graphical cues, other video cues, and / or audio cues with parameters that are not part of the original text. By instructing the user to pace their breathing based on the Ocue Additional respiratory biofeedback can be achieved by the user performing their own breathing. It does not need to be monitored at the time, but actually monitors patterns with externally defined parameters. These cues should be sensory, but preferably non-contact. and modulated to allow the user to subconsciously synchronize their breathing with each pattern. Light or sound with a strong pattern (e.g., the sound of waves or lapping tides, sounds from nature, or instrumental recordings) may include audio material).

[0235] Returning to Figure 33a, another such process can be further described as follows: Currently, the subject's respiration rate is measured using bio-motion sensors through time- and frequency-based analysis. This is estimated by processing the data and calculating the user's breathing rate. A set of rules is used to distinguish reference patterns in the signal and provide an output stage. The filter bank and associated signal processing blocks extract the higher frequency motion signals. The dominant respiratory frequencies are determined by the Fourier transform. can be identified using a transformation and tracked, for example, at 15-second or 30-second intervals. The calculation of the spectral content of the signal is done using Fast Fourier Transform and peak detection (frequency domain). or by using discretized wavelet transform, appropriate basis selection, and peak detection, etc. This is done through time-frequency processing. The remaining low frequency components are also processed to create a longer time scale. It is also possible to process respiratory rate vectors (1 Hz).

[0236] This process can also create an adaptive baseline for the user, over a period of time ( For example, median, mean, interquartile range, skewness, kurtosis, minimum respiration rate and Respiratory rate parameters such as maximum respiratory rate can be investigated, primarily (but not limited to) The time when a person is asleep (or in bed) is the target. The system is capable of analyzing and tracking respiration rate and respiration rate variability. Ability to track inspiratory and expiratory waveforms, as well as short-, medium-, and long-term respiratory variations .

[0237] Once the user's breathing rate is calculated, audio and / or video cues are added to the calculation. Alternatively, these audio cues and / or Or the video queue is not calculated, but a predetermined speed, statistical data from this user data, statistical data from other users, or data obtained from the general public not associated with this device Visual and audio cues can be provided to the user based on the statistical data collected. It is adapted to guide the user to a low and steady breathing rate. For example, this For a normal user, this can be 6 breaths per minute to 9 breaths per minute, but the subject's detected This can be adjusted to suit the breathing rate / amount of movement required and can be in the range of 2 br / min to 25 br / min. For practical stress reduction, the highest suggested respiratory rate target is 14 br / min. The light / sound sequence is based on the user's breathing rate and breathing rate trend information. The system is designed to gradually bring the level to the target level adaptively. Users cannot adjust their speed below 20 br / min, and If the system observes that it is unable to obtain the speed of the user, this is may indicate that the user is unwell or suffering from respiratory problems, User notes in the form of risk assessment reports available online or through smart devices This can be saved as a PDF and shared with the user's doctor. This can be used as a basis for discussion. All sleep pattern reports are available from smart devices or is available online. This report can be presented in the form of a histogram. Sleep Score provides feedback on a user's sleep patterns following a sleep session. It is the mechanism used to represent

[0238] Further examples: The subject is monitored for 30 seconds and breathing is detected at 17 breaths per minute ( As discussed hereinabove, this detection is performed by filtering the biomotion signal. filtering and spectral and / or time domain analysis to separate respiratory components. This is achieved by:

[0239] Assume that the user is new to the system and has no "history" or trend data available. Assume that an audio file is recorded at a target rate of, say, 14 or 15 breaths / min. This target speed should be 5% to 20% lower than the acquired speed, More specifically, it should be 10% to 20% lower, i.e., 10% lower. In some cases, the starting speed can be limited to 12B / min to 14B / min, respectively. If the respiratory signal cannot be estimated, the default opening rate is 10 br / min to 14 br / min. If historical user data is available, the starting speed can be selected. Or the average speed two minutes after receiving the sound is read from the database (data store) and It is used as a period estimate.

[0240] The particular sound sequence used can vary, but one example is the sound of the ocean. Based on the sound of waves breaking on the shore, the sound file can be adjusted to other periodic speeds without changing the pitch content. It can be stretched and squashed (compressed) to give different degrees.

[0241] When the sound / music is played to the user at the initial speed, the subject consciously or subconsciously The patient begins to match its breathing rate to the provided reference rate. Then, slowly increase the target breathing rate cue to 6 breaths / min (10 breaths / min) over 10 min. The range can be ~3br / min, but 6br / min is considered acceptable for most subjects tested. The respiration rate is reduced to a target respiration rate of 100 bp (which is generally sedative). This reduction may be gradual or stepwise. The system will switch off if light sleep is detected. The volume reduction is discontinued if the user is not detected as having gone to sleep. In this case, the system will turn off after a predetermined time, for example, one hour.

[0242] In one embodiment, the system begins reducing the sound after 50 minutes and reaches a minimum of sound by 60 minutes. The system can be turned off to complete the program and maximize play time. To achieve this, turn the volume on for the next period, i.e., 10 minutes after the target respiration rate is reached. You can keep checking every 5 minutes until 10 minutes before you have to shut it down. When this happens, the feature will close and the application will return to the sleep screen.

[0243] Sensor feedback may be used to measure the user's breathing rate using audio and / or visual cues. This is used to monitor whether the call is slowing down along with its queues. The reduction in suction rate is designed to be smooth in nature (i.e., no sudden jumps). It is designed to be a certain percentage below the acquired (detected) speed. If the detected user's breathing rate is stable at a rate higher than the desired rate, or An exception may exist if there is a sudden increase to a previously high rate. For example, He was breathing at 17 breaths per minute and was being guided to reduce this to 13 breaths per minute, but If the rate suddenly rises to 25 breaths / min, the system will not track this higher rate. (Faster breathing rates tend to arouse the user rather than relax them.) Alternatively, the controller may use audio cues and / or visual Temporarily halts any changes to the frequency of queues, allowing users to adjust their speed to match the frequency of those queues. The frequency drops to a level close to the last frequency of those cues before the downward change in frequency resumes. Alternatively, the controller may wait until the user's breathing rate increases. Cue frequency to be the same as or a certain percentage (e.g., 10%) below the breathing rate. Increase the frequency to "get" the user's breathing rate more easily, and then reduce the frequency again from there. The device can be programmed to start

[0244] The system will maintain this mode for 2 to 20 minutes depending on the speed of change, regardless of the user's response. It can be programmed to operate at a predetermined time and then stop. What didn't work for me was that users had particular difficulty following the audio guide. and the continuation of that process may hinder rather than help the user fall asleep. This may indicate that there is a possibility of

[0245] In another embodiment, a beach wave sound sample is generated with a cycle rate of 5 seconds (equivalent to 12 breaths / minute). This sound file can be stretched and scrunched without changing the pitch content. Other periodic rates can be obtained by changing the

[0246] The sound files are collected from a unit incorporating real-time respiratory rate and RF biomotion sensors. Get their feedback on your sleep status with this simple app process The application can store various parameters in CSV (Comma Separated Values) format for post-analysis. Returning to Figure 33a again, the iterative process shown in Figure 33a The process may include the following: 1. The periodic tone guides the subject's actual breathing rate downward toward the target breathing rate. The offset values and epoch lengths quoted in are starting points and can be modified through experimentation. It is possible. a) The default target is a respiratory rate (BR) of 6 breaths per minute, but the GUI (Graphics) The global user interface has a user-configurable target BR. b) The periodic sound is generated at a B of 0.5 breaths per minute lower than the subject's current epoch average B. R, i.e., direct the subject's current epoch average BR downward toward the target. The offset value can be preset or determined based on the optimal starting point from the user's breathing rate. It can also be determined empirically through differential testing sessions. c) The periodic sound BR is updated every epoch (i.e., next time it switches up or down). (Replaced). d) Starting conditions: Assume a BR of 13 breaths per minute for periodic sounds. For 4 epochs The subject's BR was monitored, and after the start condition of these 4 epochs, a periodic sound was generated from the subject's BR. minus an offset. This is done in an attempt to "get" the subject's BR. do. e) While guiding the subject's BR downward, the subject's epoch-averaged BR is >4 epochs. If the periodic sound BR remains above 1 breath per minute during the test, the periodic sound BR is This allows the subject to move to the current BR minus the offset. This is to try to "get" BR again. f) The overall amplitude of the periodic sound decreases over time as sleep is detected, Go-to-sleep logic can be implemented. Sleep for 10 epochs. The original volume is reduced by 1 / 10 for each epoch, and the subject If the subject remains awake for a period of time, volume reduction is temporarily stopped and the subject is allowed to go back to sleep. Hold the volume level until

[0247] Cycle Variation In some versions, the device will recognize the end of one file and the beginning of the next. Single with short padding between them to prevent clicks or jumps between A sound file of 0.5BR step length can be used. 10 breaths per minute to 15 breaths per minute at a BR (i.e., 10, 10.5, 11.0, etc.) Can be pre-configured with a set length that matches the BR per minute. Short file lengths are , which causes a small gap between the end of one cycle and the start of the next. With this in mind, each periodic file has a total number of cycles, but This allows you to connect a continuous sound file of approximately 30 seconds in length. The occurrence rate can be reduced to a minimum. This effect depends on the SmD hardware. Proper buffering in software (e.g., to facilitate seamless looping) This can be addressed by

[0248] Version Various further versions may have one or more of the following features. User selectable target breathing rate. Ability to select different source sound files. Limited or full breathing coach logic as specified above. 2-step algorithm logic pattern. The sound cycle rate can start at 12BR. and continue there until the patient's breathing rate reaches or falls to 12.5 BR or less. The sound period rate is then reduced to 10BR. Three or more steps may also be performed. It is possible. Use a constant sound cycle rate, for example 10BR. In this case, the rate from the bio-motion sensor Only using real-time feedback reduces the volume of sleep status feedback. is.

[0249] Data storage Data acquired during the relaxation session were collected in a CS with four rows of data, one per second. It can be saved as a V file. I. Daily Stamps II. Subject Status III. Subject breathing rate IV. Sound (target) breathing rate Raw bio-motion sensor I / Q signal levels are also stored at a sampling rate of 16 samples per second The data is then passed through the application to the GUI, where it is then put to sleep. A report can be generated. Optionally, the raw data can be stored in a "zip" file or similar. It can be stored in a compressed format.

[0250] Data analysis Data analysis for each subject was performed in one spreadsheet (Excel) file per subject. This can be extracted from the raw data file and then stored in the Each event plotted on one graph as BR, target BR, and subject sleep status. This can include the first hour of data from the POC. Separate graphs for each night. can be done.

[0251] The mean time to sleep (sleep latency) under each configuration was measured for each subject, if available. A summary Excel file can also be generated that compares the results with the summary comments from the other participants. do.

[0252] This relaxation process optionally reduces stress during shorter periods during the day. / Can be used to promote relaxation.

[0253] The user's heart rate can also be used along with breathing rate to indicate a relaxed state. For example, there may be a correlation between these two parameters, indicating a more relaxed state. greater coherence (e.g., time-domain or frequency-domain scales) can be used when there is a

[0254] Software - Specific Embodiment - Daytime Relax (Assisted Meditation) As mentioned above, this system is similar to the "relaxation for sleep" process. Both will have a "daytime relaxation" process that uses similar features as mentioned above. This process can be performed by a SmD processor. The "Assisted Meditation" process involves guided breathing exercises accompanied by a selectable range of sounds and / or lights. This is intended for relaxation at any time, but especially The purpose of this feature is to relax you in the evenings as you approach bedtime. Optionally, but not necessarily, the user's breathing rate is used to determine the initial rate of the selected sound. You can set the degree of motion. There is no need to connect a hardware bio-motion sensor. This feature can be used anywhere. This relaxation feature is a relaxation feature for sleep. It follows a similar logic to the X-process, but with some differences: "Relax" breathing The speed reduction feature is a "relaxing sound" (selected by the user from a range supplied by the app). The device synchronizes the sound (selected by the user) with the user's measured breathing and modulates the sound to simulate the user's breathing. Slow down. Volume reduction is not determined by the user's state of alertness. Instead, The volume reduction can follow a predetermined course. In some configurations, this can be achieved by a user This "meditation" can guide users to breathe at a specific rate and relax. This requires interaction with the device. This keeps the user awake.

[0255] The audio speed can be initially set (for example, to 12 BPM) (this can be changed This speed may vary (for example, 14 BPM or some other value). Then, it can be reduced to a target minimum value (e.g., 6 BPM or less) according to a predetermined reduction path. Volume step reductions can optionally occur every 2 minutes. The user can optionally set the length of the relaxation period. You can then determine the rate of volume reduction for the audio file.

[0256] If the user interacts with this process and selects a different audio file, The playback speed can be reset to the initial speed (e.g. 12) and the logic will (The parameters are refined.) By closing this feature, the program The layback is also completed.

[0257] Further options (see above) are: · Provide high quality sound (file type AAC, etc.). · Facility to download additional sounds in the future. -UI to select and play various sounds. Volume default value when user interacts with process during session Return to (14 breaths per minute). · Streams audio to speakers (if connected).

[0258] One exemplary process may be performed by a processor as follows. The user selects the "Assisted Meditation" option. Audio is not available at the default breathing rate of 12 breaths per minute (i.e., the maximum available It will play for 2 minutes at 2 BPM slower than the original. At the end of the 2 minutes from the previous step, increase your breath every 2 minutes until you reach a minimum rate of 6 breaths per minute. Reduces playback speed by 2 breaths each time. Once the minimum respiration rate is reached, remain at this rate for 10 minutes. At the end of the 10 minute period from the previous step, the volume will increase by 10% every minute for 10 minutes. Reduce the noise.

[0259] If the user interacts with the app and selects a different audio track, The back speed is reset to 12 and the logic restarts (parameters are refined). You can end the playback by closing this process. Cut.

[0260] A suitable example of this process can also be considered with reference to Figure 34. This example This feature does not require acquisition of the user's breathing rate before or during activation. This feature is a default call. Start with an absorption rate (e.g., 12 br / min) and follow a rate reduction path, then reduce the volume. The mechanism of action can be followed.

[0261] Referring to the example of FIG. 34, in 3401, the processor starts the sound file at an initial speed ( For example, 12 BPM) is repeatedly played back. Time passes during playback (for example, wait 2 minutes). If this speed is greater than a minimum speed (e.g., 6 BPM), The speed of the note is set to, for example, 2 BPM in 3404 by the sound period extension process described above. The sound file is then played again in a loop at 3401. In 03, if the speed is not greater than the minimum value, the sound file is repeated in 3405. A waiting period is implemented while the loop is being played. A gradual volume reduction process (e.g., volume reduction) occurs until the volume reaches 0 or off within 10 minutes. For example, 10% per minute can be implemented.

[0262] The reduction in breathing patterns due to such a process is related to the graphs of Figures 35a and 35b. This can be further considered as a predetermined speed in the daytime relaxation feature. The graph in Figure 35a shows one embodiment of the reduction path. Figure 35b shows a controlled reduction of 14 br / min to 6 br / min. The graph shows a reduction in playback from 12br / min to 6br / min with Audio Energy The graph in Figure 35b shows the daytime relaxation time. The volume reduction towards the end of the process is also shown.

[0263] Software - Conceptual personalized sleep and environment advice As mentioned above, the present system generates messages regarding sleep advice / For example, the system may be configured to output a sleep-related signal from the sensor signal. It builds an understanding of the user's sleep patterns through analysis and questionnaires, so it can be customized. It delivers personalized advice based on the user's sleep habits through the use of an "advice engine." In some cases, it can help improve sleep quality, such as by treating sleep-related health problems. Other products (e.g., anti-snoring devices, sleep apnea therapy devices, CPAP devices, etc.) It includes a diagnostic capability that can connect users to an advice engine for other sleep issues. The information generated by one or more processors of the system can help identify This advice covers the benefits of good sleep habits, the best environmental conditions for sleep, and how to improve your sleep. It can be designed to inform the user about their daily activities. reliable and insightful to help users sleep better and keep users engaged with the system as a whole. The system distributes information to users, the local population of system users, or to tailor advice to the individual patterns of the global population of system users. , a learning classifier using Bayesian methods and / or decision trees, etc., can be implemented. Prompting users to respond to electronic queries embedded in task / advice nuggets The user's response can be used to guide / trace a path through the contents of the decision tree. This can be done.

[0264] A user's detected sleep patterns may also indicate risk for severe sleep problems. If a major sleep problem is detected, the system will connect you to expert online or offline resources. In-resources (e.g., expert advice articles, access to relevant forums, or and may recommend a connection to a sleep specialist or sleep center. This connection can be facilitated by smart devices (e.g. This can be facilitated by a mobile phone (e.g., a cell phone or tablet) or a PC. The links on the computer of the patient may not be used to communicate with such professionals or to download sleep-related information. For example, a user can initiate communication to load or access the The system prompts the user to send a report to the specialist along with the detected sleep information. The expert can then trigger a response to the user through the system. For example, a doctor may receive a diagnosis generated by the system described herein. and providing a medical report regarding the user's sleep health based on the sleep report received and reviewed by the user's physician. A professional report or expert opinion can be generated and transmitted. This can be done at the bedside. Through one or more of the system servers, such as the device BeD, a dedicated web page, etc. This can be facilitated by means of a smartphone or through communication via SmD.

[0265] The creation of such reporting elements can have multiple paths and can be based on the detected sleep problem. For example, the report feature may be a PDF that the user can print / save. Or other document formats can be delivered on the screen. (Raku) Patients with underlying insomnia but with poor sleep hygiene and / or suboptimal bedroom environment For users with sleep problems, this path leads to an advice engine that tries to improve the user's sleep. The report can include trend data for sleep parameters and what the main sleep drivers are. A description of the benefits of this advice and any advice given. It can show the changes (if any) in the user's behavior.

[0266] For example, a typical report may include any one or more of the following information: Do you have problems falling asleep or staying asleep? How many days a week do you sleep? Sleep duration Fragmentation level Shallow / REM / Deep amount Detailed example reports are also shown in Figures 54a, 54b, 54c, and 54d.

[0267] Figure 36 shows the overall flow diagram for advice generation. , BeD, SmD, and cloud server(s). In 3602, breathing and movement (and optionally heart rate) ) data can be detected from the user. This data and / or its sleep-related Interactive analysis (e.g. sleep staging) can be sent to the advice engine. Alternatively, the sleep room environment information (e.g., light, sound, Temperature, humidity, air quality, etc.) can also be provided to the advice engine. additional information such as local weather (and location data, if available) The information can be analyzed at 3608. At step 0, the generated or selected advice based on this analysis is queued for delivery. In 3612, one or more different delivery means (e.g., Website, Text Message, Push Notification, Voice Message, Email, SmD Advice can be delivered to users via the app notification message, etc. At 614, the user may select a query or electronic advice associated with the electronic advice. The response can be used to generate further advice. During advice processing, sleep characteristics can be It can identify various characteristics and trends in the data that may identify characteristics and patterns. Based on these characteristics and trends, the proposed system and method are These signals may be, at least in part, The minutes can be processed on a backend server.

[0268] Figure 37 shows the software in one or more back-end cloud servers. This shows one process that can be performed by the advice engine. A service can be formed by multiple services running on multiple backend servers. This is the case, for example, with push notification services from Apple or Google. The back-end service can operate in conjunction with the client server. Push notifications can be sent over cellular / mobile networks or other wireless networks. The advice database can be distributed over a wired network. Can be separated from the user database for scalability reasons. The service engine is responsible for advice generation, scheduling, and It can be a back-end component that implements the routing and distribution logic. Therefore, in this example, the advice engine service module 3702 receives The advice engine can receive advice requests from the user data engine. Access user data, measured sleep information, trends, etc. from the service module 3706 This information can be stored in the user database 3708. Based on this information, the advice engine can choose the best advice based on its advice selection logic. Advice nuggets can be selected from the advice database 3704. The resulting advice nugget is then associated with a particular user and then The user data engine can store the user's advisory information in the database 3708. Provides push notification queue 3710 with push nuggets, scheduling, and delivery information. This queue service can then provide the necessary advice communication information to the user. The notification can be provided to the push notification service 3712 in preparation for delivery.

[0269] The advice engine process flow methodology is illustrated in the diagram of FIG. 39 and the state diagram of FIG. 40. This process can be considered as follows: Evaluation state 3902, Recognition state 3904, Ad The status may include a device status 3906, a task status 3908, and an audit status 3910. These conditions can be discussed with reference to the following discussion of FIG.

[0270] In the initial state, a bedroom evaluation stage 4002 can be created. allows users to instantly receive recommendations based on their first night's sleep. These recommendations are specifically aimed at optimizing the bedroom environment and sleep-related detections mentioned above. This stage can usually last for 3 to 4 days (i.e., B If no problems with the user's sleep are detected, The user receives a nugget-like "wizard" that provides information about sleep. In other words, if no problems are found, The fruit can be supplied as nuggets, which have no real effect on the user's sleep. This can confuse users who may not want to be informed about environmental factors that are not For this reason, some specific advice is given to Exclusions can be based on the detection of conditions.

[0271] After the initial assessment, e.g., after 4 days, SmD may ask questions about the user's sleep log (e.g., trends). By detecting the problem, the sleep evaluation stage 4004 evaluates the sleep of the user. The system can begin to recognize more detailed information. If not, nightly sleep monitoring will detect environmental conditions and sleep indicators / parameters / stages etc. You can stay in the sleep assessment phase.

[0272] If a problem is identified, the user is given a pre-cautionary alert for a period of time (e.g., up to two days). This will prevent the user from being confused or put to sleep. Without being prompted to program, users are able to have a one-off / unlucky night. If the problem disappears, the user returns to the sleep evaluation phase. If the problem remains , the sleep advice phase becomes active again. This phase continues for the following period (e.g. For example, it may last for approximately 3-5 days depending on the conditions detected and the content available. If a positive (improving) or negative (deteriorating) trend is observed, the user can In the direction stage 4009, trend feedback can also be received.

[0273] In some cases, previously detected problems have been fixed or are no longer detected. If the device detects this, the process moves from advice stage 4008 to assessment phase If not, the process may continue or proceed to step 4010. Return to the advice phase and provide further or secondary advice suggestions in the advice phase. It can be generated.

[0274] In some cases, the user shows no improvement (i.e., sleep-related problems recur) If the device detects that it has been detected, the process proceeds to task stage 4012. These tasks are longer-term programs that address several issues. These include, for example, increasing exercise levels and reducing caffeine intake.

[0275] In short, over a period of time, the advice engine monitors the user's sleep patterns, Personalize your address based on changes in your sleep patterns, diary entries, and personal profile The process recognizes the problem it monitors and if this problem persists, If the problem persists, the process uses advice nuggets to inform the user about these issues. The process moves to the advice phase, where the patient is informed and corrected for the sleep problem. However, if the user does not respond to the advice, If not complied with or the problem is no longer detected, the system will enter a review period for several days. Advice can then resume addressing the issue as usual. If it is no longer detected, the system can return to the evaluation phase, and In this case, sleep problems are not detected, but the user is monitored. If complied with, the compensation policy can be implemented. These processes are illustrated in Figure 38. is also shown.

[0276] advice As mentioned above, the advice engine is responsible for managing and generating all advice content and Implementing business logic and scheduling advice to the push notification engine The input to the advice engine is usually provided by a Processed data from the BeD and / or SmD, such as data stored in a database This input may also include advisory feedback and / or feedback from the user. or user data and state information (e.g., the state of the advice process, Fig. 38, Fig. 39, and 40). The output of the advice engine may include a hypnogram and Advice Nuggets and / or Hypnograms and Advice Nuggets This can include advice annotations provided by, for example, an SmD or cloud server. Or it can be communicated via an interface to a conventional server. Advice through communication interfaces (e.g., push engine interfaces) It may also contain content / nuggets and advice scheduling information. The device engine is used exclusively in SmD or BeD devices (with graphical displays) and / or enabled using the sleep-related processing features of SmD) It is possible.

[0277] The advice engine typically triages specific pieces of advice (nuggets) for improving sleep. These can be used to specify combinations of parameters that affect the via text message, email, or application notification (e.g., push notification) The actual advice is stored in a queue for later delivery to the selected user. For example, excessive "light sleep" may be Periods of sleepiness (stage 1 / 2), restlessness, and wakefulness are detected in the user's early morning hours. Assume that the advice engine detects this condition (detected by the light sensor) This can be used to identify situations that occur at the same time as high light levels (such as The advice given in this case is to use blackout curtains (in some cases (Also offers the ability to purchase online). The light sensor detects this high light level Whether the light is due to sunlight or artificial light (e.g., incandescent bulbs, LEDs, fluorescent lights, etc.) The system can also detect if the patient is experiencing SmD symptoms and adjust the advice accordingly. Search for information on sunrise, sunset, and other dates using online services or lookup tables. Other parameters from the model can be estimated.

[0278] Therefore, the advice engine can be configured to run on multiple servers running on multiple backend servers. This may include or have access to services (e.g., Apple / G Integrates with push notification services (from Google) and other operating systems The back-end service can then follow a client-server model. Push notifications can be delivered over a mobile or cellular network. Advice databases are often used for flexibility / scalability reasons. The advice engine can be separated from the user database. Back-end components (e.g., For example, a processor service on a cloud server.

[0279] As a further example, the advice engine may be configured to measure BeD and resolve SmD. an estimate of the user's current and historical sleep data as analyzed, and Enter lifestyle data and a record of advice previously given to the user. , delivers advice to help users improve their sleep. Informs users about the benefits of good sleep habits, the best environmental conditions for sleep, and daily activities that help with sleep This advice keeps the user engaged with the system as a whole. We deliver reliable and insightful information so that our readers can

[0280] The advice engine then uses one of the following interfaces: Any one can be implemented. Advice Engine Content Interface: Using the Advice Engine's Logical Process Advice engine library and advice engine that allows you to select advice The interface between the application content. Data Access Layer: This is the back-end repository (e.g., the user database) It is the interface between the service server and the advice engine. Notification Engine: This allows you to send notifications to users via smart devices etc. To perform Noh.

[0281] Advice generation by the advice engine can be further explored by the following example: Cut. (1) Light Level and Sleep Disturbance Advice: (a) Higher than average ambient light detected If the user covers their eyes or turns off devices with lights, LEDs, etc. (b) A message can be generated suggesting that the user consider using blue light. If detected, such devices should be covered and blue light can disrupt sleep. A content message can be generated that identifies the reasons why it may be effective. An increase in bells is detected around sunrise, waking the user up and disrupting their sleep during this time. If the device detects this, it may recommend blackout curtains or other window coverings. If a flashing light is detected, a message can be generated. The content suggests that you consider checking whether notifications are available or turning off notifications on your smartphone. A message can be generated to confirm that the (2) Sound Level and Sleep Disturbance Advice: (a) Road noise, garbage / bin collection noise, and and / or if high background noise is detected by microphone sound analysis, the user may need to wear earplugs or Generate a message suggesting that other sound control / masking with background white noise be considered. If snoring is detected (for example, by the user or their bed partner), If detected by crophone sound analysis, the user may consider snoring reduction aids. or otherwise suggesting to seek the assistance of such SDB in reporting. It is possible to generate a page. (3) Temperature and sleep disturbance advice. (a) Records room temperature and determines whether the user is falling asleep too late. If the device detects that it is too hot or too cold, for example, A message can be generated suggesting that the user consider changing the temperature. (b) Recording the room temperature and detecting a wake-up during the night, if the user feels, for example, excessively cold. or suggesting that you consider changing the temperature in a room that may be excessively warm. (c) Recording the room temperature and detecting the awakening of the person in the morning along with the temperature change. If the temperature is too high, the user should turn on the boiler / heater as sudden temperature changes may disturb sleep. A message can be generated suggesting that you consider changing the start time of the data. Generates control signals, optionally to a thermostat and / or air conditioner controller The temperature (and / or humidity) may also be transmitted to a temperature (and / or humidity) control device such as (4) Sleep pattern advice: The device will advise you on sleep patterns, such as short sleep duration, fragmented sleep, etc. Sleep, if it detects poor sleep efficiency, the user can choose from a variety of sleep hygiene advice It can generate a message suggesting that you consider these suggestions. The event is linked to a problem detected in an environmental event such as any of those listed above. When used, environmental adjustments can be included.

[0282] In some cases, location data (e.g., GPS or other location-aware information) The advice is generated based on the location advice. For example, by evaluating location data, advice can be generated. can be based on the actual sunrise time at the user's location. The device can determine if the user is traveling and can detect jet lag or the user's new The room environment, as well as pollen counts, daytime or nighttime temperatures and other factors that may affect sleep It can provide appropriate advice on managing weather and other weather-based parameters such as humidity. The phase of the moon (e.g. full moon) can also be taken into consideration and used to adjust the advice. It is possible.

[0283] In some cases, the advice engine may use any of the following: That is, the back-end infrastructure (e.g., one or more servers) server), an application that comprises multiple cooperative advice subunits that run on a back-end server. In the relational database running on the back-end server, Advice database hosted on the server, advice push running on the server A graphical user interface running on one or more smart devices. GUI-based advice display mechanism and / or extensive user experience These implementations are described in the above. Distributed across functional blocks.

[0284] Advice messages or advice nuggets come in two forms: leading and following. Lead nuggets are advisory experts who are responsible for the problem being addressed. These can be related to the alcohol level and cuff pressure. excessively high energy levels or excessively low exercise levels and / or suboptimal environmental conditions Adaptable nuggets can contain information that is being addressed by the advice engine. These can be linked to specific causes of sleep problems and contribute to restful sleep. The user's sleep patterns as shown by the hypnogram, such as the length of REM and deep sleep, the number of awakenings, etc. These problems can be related to the sleep patterns of users. This allows the system and repository to share the same identification information for each issue. Each problem can be mapped to a database so that the problem can be solved. Analyzing the existence of problems and evaluating the relevance and content of messages to communicate to users It may have a specific detection method.

[0285] The processing of the advice engine is further discussed with reference to Figures 41, 42, and 43. The advice engine may perform any one or more of the following operations: It can have logic. (1) Add the measured sleep data to the user profile. This is required to build the file and then generate personalized advice. (2) Request user profile data and add it to the user's profile, which includes: required for both building a user profile and subsequently generating personalized advice . (3) Obtain sufficient user and sleep data to generate a user profile. This is because the system provides generic cognitive advice when sufficient personalized data and advice are available. Engage the user until the complete user profile is available. This is used in conjunction with the current record to generate personalized advice. (4) Delivering personalized advice after an initial data collection phase. (5) Personalized advertising based on trends in the data captured and previously delivered advice. Deliver the vice. (6) Allowing users to improve their sleep more significantly and closer to their goals. Maintain a history of previously given advice. (7) Learning user habits and applying common sense experience to specific users.

[0286] As shown in Figure 41, initially, the advice engine mainly performs the problem analysis process 4. At 102, the detected patterns are shared with other users, the described techniques, or external sources. Sleep problems can be detected by comparing the normalized data obtained from the However, over time, as user usage increases and historical user data is collected, It is preferred to compare recently detected sleep patterns with typical historical data for a particular user. This allows the company to abolish "industry standards" in advance. This allows for further customization of advice. Given a history of sleep recordings and advice, the system can address the user's most recent sleep recording issues. The processor processes the most relevant advice template available on the system. In this regard, the most relevant criteria for selection are: The following parameters are considered: existence, tendency, extent, sequence, and substitution of the problem with its causes; This may include language, user type, and / or stage. Trends from can be determined in a trend analysis process 4106.

[0287] In some cases, the advice engine library will The logical process for generating the data is defined by the following measured parameters (also known as principles): It can be based on, among other things, REM duration, sleep duration, Time, wakefulness (number and / or duration), SWS (deep sleep) duration, and sleep onset duration These predisposing factors can be the basis for several possible problems. 4104, a problem analysis process to identify the causes and the comparison between the causes and the standard data. This engine is the basis of Process 4102. Each of the underlying factors (reference factors) is a standard (uniform The issues that seem most relevant based on how far they deviate from the general standard ( The system assigns a tag (or tags) to each user based on the relevance of the problem. Continue tagging users as having issues until the number of users drops below the threshold.

[0288] The processor of this engine correlates users with trends in a correlation process 4112. The trends can be classified as: no change, very good improvement, good improvement, stable. The trend can be one of the following: stable, worsening, or very worse. The trend is based on the previous The identified trends are based on at least one possible cause and / or A queue of advice can be generated that selects the most likely cause. First, The most likely cause is the measured factor that deviates most from the norm (general standard). The cause process 4109 determines the cause based on measured factors. The causes of sleep problems 4110 can be assessed using the (1) Environmental factors (default) including (a) temperature, (b) light, and / or (c) sound (2) lifestyle (enabled by specific issues), i.e. These include (a) stress, (b) food and drink, (c) caffeine, and / or (d) alcohol. First, all causes can be weighted by a factor of 1.00. How any problem is affected by the cause (or measured predisposing factors) The correlation coefficient is used as a knowledge base about the problem and its causes (how it is affected by the It can be applied between

[0289] Figure 42 shows the advice process when a detected condition results in the generation of different advice content. The diagram further illustrates the "Problem" It also shows the relationship between sleep and "cause" in more detail. Become enlightened to optimize your sleep environment. Behavior improvement paths are advice nuggets For example, question 4202 asks about REM time, deep sleep time, and REM or deep sleep can be detected using the number of interruptions and / or the number of interruptions. (general standards and / or The interruptions can be detected and evaluated by comparison with thresholds (based on user trends). The measured information can be compared to a threshold to determine whether there are excessive interruptions. A probabilistic analysis process for problems with the information or input data and the relationship between the problems and the causes 4204 4206 (e.g., measured light levels, sound levels, temperature levels, and other user inputs) 4208) over time. Delivery of various advice messages regarding detected problems over time The progress of the program is selected based on the association of these messages with various causes and detected problems. It is possible.

[0290] Figure 43 shows the advice engine that generates advice and receives feedback. The stored user sleep record 4302, advice content 430, and the like, which are managed using the 4, and evaluation or baseline data 4306 (e.g., detected problems, causes, and trends) The data relationships are further shown in the user advice history data 4308 (e.g., sleep, environment, previous advice, and their feedback) are used to identify detected sleep problems, causes, The results may be based on any one or more of these data, including trends and trends. How problems and causes are correlated with the advice engine analysis for advice generation This paper shows how the analysis contributes to advice engine analysis.

[0291] As mentioned above, the advice engine library displays the user status as shown in Figure 40. The user can move between and within various states as specified in the In some versions, the following states can be implemented: (1) Normal / 101: This is the advice engine's attempt to find something bad about the current user's data. If all measured sleep hygiene factors are within the expected range, This situation can continue indefinitely. (2) Awareness: If the advice engine detects a problem with the user's data, it The library will start tracking the problem and enter a recognized status. If the problem is still detected, As long as the advice is given, the number of stages specified in the advice will be used for multiple records. , and remains in this status as the most relevant. (3) Advice: The advice engine identifies a specific problem in the problem content data. The number of consistent sessions in the user's data corresponding to the specified maximum sequence number If the system still detects the problem, it moves to the advice phase. During this phase, the content can be more specific, but from the advice engine's perspective, the behavior are quite similar. The main difference is that the content is now delivered in two parts: The first is associated with the problem, and the second is the detected potential associated with a cause or most likely cause. The maximum possible sequence number (e.g., all previously communicated advice) has already been sent. If so, the system can trigger the task and move to the task phase. do. (4) Tasks: During this phase, the system creates specific programs with daily tasks. This phase is defined in the system as follows: , proceeds for the entire duration of the task program. At the end of the task program, the user The employee receives a report showing progress, improvements, and highlights of daily tasks. The program returns to the regular phase and triggers a task program to obtain multiple records. If no other problems are detected, the system will stop monitoring for the problem. If not, the system moves to recognize new problems that have been detected. do. (5) Review: During the recognition or advice phase, the user repeatedly gives negative feedback. If the user replies with a question or the problem is no longer detected, the user moves to the review phase, where a number of From this phase, if the issue reoccurs, you can stay in the review phase for a period of days. , so the system returns to where it left off, or the problem disappears entirely. This stage allows the system to return to the regular phase. The seat engine ensures that newly established environmental conditions and behaviors are maintained as new habits for the user. This will enable the organization to ensure that the measures are implemented successfully.

[0292] Referring to FIG. 44, the advice engine is configured to manage various components of the system. These components may include: (1) Server-side component 4402: This runs the advice engine. and scheduling and delivering advice to users and advice engine alerts. , an advice engine library 440 that ultimately accesses the published content 4406 4 includes a process having software etc. responsible for generating call-specific functions within Its components are as follows: (a) Advice Enqueuer (b) Advice Dispatcher (c) Advice Content Warning Generator The advice enqueuer and advice dispatcher are the communication components between the two components. This creates a queue-centric workflow pattern that is driven through the advice generator. The dispatcher is responsible for determining which records are coming in (which are queued) and which trigger the dispatcher. Let me give you some advice on how to send a push notification to the service and how it will ultimately be triggered. . (2) Advice engine content and management tools: a set of software components The content editor 4412 allows editing of the advice content 4410. The content editor has access to both production (live) and local data recordings. , a publishing tool 4408 to pre-populate the advice engine content database A sequence of recordings published to various environments (production / staging) via Provides a mechanism to play back, i.e., manage, advice engine content This makes it possible to: (3) The advice engine publishing tool 4408 is a tool for publishing advice in its entirety. Evaluate the quality of the current advice, version it, and use it in various environments. Advice engine publishing tools act as a mechanism for deploying Access to both the L (Data Access Layer) and Advice Engine Library 4404 The Advice Engine Publishing Tool (SQL You can read the content file (stored in XML format, etc.) The easiest publishing tool to write to a "live" database. A typical form is a SQL script that is executed using a SQL Server management application. It is possible. (4) The advice engine library 4404 is responsible for generating online advice. Its main concern is the user records and profiles. The system selects the most appropriate template from the list according to the file. Have logic to accept that the problem may be due to the most likely cause. This is not always true, but knowledge of how to improve such causes is The base can be improved over time as mentioned above. It is the main and most important component of the vice engine.

[0293] FIG. 45 illustrates the architecture of an example push engine 4502 and the advice engine. 4504 and one or more messaging notification servers 4506. Some example notification servers are iOS, Android, oid and Windows (登録商標) It may have an operating system Once the advice is determined, the advice engine processes it. requests that the advice generated in the cloud be sent to the user. Whether or not a nugget is sent to a user is based on the advice engine logic. The scheduling and method of advice delivery can also be determined by the advice engine. This uses a "delivery" method such as a push notification service to deliver the advice. The advice generator 4508 process generates advice nuggets / messages. The advice repository 4510 receives advice from the advisory engine and queues them in the advice repository 4510. The Device Dispatcher 4512 process (e.g., a communication service application) Receives messages from the notification engine 4514 (through a programming interface API) The notification engine then forwards the message to the messaging notification server 4506. Communicate with the saging notification server.

[0294] Example scenarios for advice As mentioned above, this system allows users to upload their records to a cloud server. The system then uses this historical data to generate a user-specific The system can also recommend behavioral changes, such as improving sleep. This can improve the user's sleep habits and optimize the user's sleep environment. The data may include generating advice to educate the user regarding: As data is collected from users, advice is based on their actual sleep habits and the User response to the actual advice delivered (e.g., the advice was helpful) It is automatically customized / individualized depending on whether the information was useful, ineffective, irrelevant, etc. These behavioral improvement paths are based on the user's response to "nuggets" or short pieces of advice. Users can receive several of these daily (and the user (You can set the frequency of your notifications). Nuggets help reinforce good sleep habits and improve your sleep quality. This will provide a path to improved sleep, including the sleep environment conditions and daily activities that support sleep. An example is as follows:

[0295] Consider a person using the system for a week. The following table shows the events that occur during the This summarizes the possible results that can be produced by the system. Event Table

[0296] (a) The system in its initial state: [Table 4]

[0297] (b) After the initial state [Table 5]

[0298] (c) Advice state [Table 6]

[0299] (d) Task state [Table 7]

[0300] Suggestions for optimizing bedroom sleep settings or sleep habits may include: (1) Initially based on standard data, then on the user's own data measurements (e.g., electronic mail) The app automatically adjusts the user's sleep environment based on the user's sleep habits (provided by the app, on the web, etc.). Immediate recommendations to improve (i.e., first-night experience). For example, the system It discreetly checks whether the light level is disturbing the user's sleep and to see if any of the following may be affecting the user's sleep and wake patterns and The system then checks one or more parameters of the user's environment, such as the ambient temperature. is significantly different from the statistical average parameters of other users or the user's own collected data. The collected data is used to application / current weather conditions, average weather trends (i.e., baseline temperatures, , which may vary depending on the season and allergy alerts). The system allows users to keep a diary (enter data into the system in response to queries). ) (e.g., asking whether the user is using air conditioning, heating, a humidifier, or bedding) By enabling it, personal data can also be collected. (2) personalized sleep patterns based on the user's sleep patterns, diary entries, and personal profile; This personal profile includes the user's name, Includes age, weight, and gender. The system provides a personalized report and a list of recommendations. It can be viewed on pp or sent by email. (3) Generate and provide a risk assessment of the user's sleep patterns and allow the user to consult a sleep physician. or suggest whether a sleep specialist may need to be consulted (e.g., "Stop Bang"). (e.g., "op-Bang" or other form of questionnaire). A list that can be the basis for discussion with the doctor. The assessment report is available in a printable PDF format. (4) Further suggestions to improve sleep – through a combination of environmental and personal routine recommendations For example, the following are generated and provided: (a) Check lighting settings, TV / gadgets, and meals before bed (i.e., best practices) Encourage them to do so. (b) When the statistically determined optimal time approaches, the system will guide you to go to bed (remind you). bedtime alarm option). (c) What to eat and drink before going to bed and when the user wakes up (food and drink) and what to do in bed Do (listen to music) and don't (don't eat or watch TV) and user Advise users on what to bring to bed together (e.g., email by phone, in-app, on the web). (d) Asking the user about settings / changes that the user can make and having the user implement them Only notice this to recommend changes that users can make, e.g., dimming the light settings. To attach. (e) Providing a "willpower quotient," i.e., a measure of the user's ability to get sufficient quality or quantity of sleep. If the user has not attempted to do so, the system will warn the user that their willpower may be tested. You can report it. (f) AP related to any problem that can help you sleep better Other products within the site or on the web (e.g., sleep bedding, eye masks, audio experience enhancement speakers) This provides users with an opportunity to investigate the (g) A discussion forum to learn from the techniques of sleep experts and others. Access is provided on the website and via email / app. (h) Recommendations regarding factors that affect sleep and ways in which sleep can be improved; and references to those interesting articles on the website and via email / app and provide it.

[0301] In another exemplary usage scenario, a user uses the system overnight. The user wakes up several times and vaguely remembers not knowing why. When you look at the hypnogram generated, the hypnogram annotates events that indicate arousal. Awakening is also shown as a single number (count) outside of the hypnogram. The device can be annotated and matched with environmental factors detected by the device. Such annotations can be displayed based on a comparison of detected events to a predetermined threshold. It can be based on.

[0302] [Table 8]

[0303] In some cases, the system may optionally include environmental data (e.g., allergies) Aggregating data from other sources (such as alerts, humidity, air quality, and related parameters) These data can be obtained from physical wired sensors or wireless sensors. It can also collect data on weather conditions, air pollution conditions, and allergy (e.g., pollen) conditions. Through "online" services such as local sources, regional sources, and trend sources How "environmental monitoring" information is used by this system An example of this is as follows: (a) Weather forecast (and historical) data, i.e., meta-environment: short-term and / or long-term weather Data can be obtained from various online sources. Cold weather can affect the face, for example. Cooling can result in significant bronchoconstriction. Therefore, the algorithm analyzes current temperature, predicted temperature, and historical data to recommend appropriate clothing and risk levels for the user Local pollution levels (airborne allergens) are recorded by an algorithm. These may be related to, for example, asthma severity. The advice provided will be based on external weather reports indicating that a heat wave (or very cold wave) is occurring. This system can be further customized if the user indicates that , you may adjust your settings to avoid providing potentially false advice. can. (b) Allergy alerts (e.g., related to pollen counts) based on forecast and seasonal A communication can be made to the user based on the value.

[0304] Further user scenarios (jet lag advice) As mentioned above, the system provides location-based advice, such as jet lag advice. In such a scenario, the SmD can generate a (b) your device time zone setting (which typically updates automatically); and (b) location-aware data. (c) large distance changes in location based on GPS or network-aided data; Based on one or more of the following: use of smart devices at unusual times of day; Based on this, possible "jet lag" events can be automatically detected. Jin evaluates the jet lag process and if the user indicates that they are planning to travel, In such cases, we can actively provide assistance.

[0305] In this process, the system moves from the current time zone to the target time zone. Exposure to sunlight at different times of the day (i.e., earlier exposure) Increase white light exposure and limit sun / white light exposure closer to bedtime in your target time zone It can provide advice suggesting that the user should By referencing the user's normal sleep patterns based on their sleep patterns, the system Changes over the previous few days (e.g., up to two weeks) can also be proposed. When the user arrives at the destination, the system automatically shifts the user's sleep to the new time zone. The system can continue to provide advice when the user returns from their trip. We can provide services.

[0306] When traveling (or just after arriving in a new time zone), the system To allow users to adjust to the new time zone, we will consider the proposed food and beverage changes, transportation, etc. For example, if a person is experiencing unusual behavior, advice can be given regarding physical activity and light exposure. If you're tired at the time, you may be more likely to snack on "junk" food. It is known that the system will recommend alternatives (e.g., flu shots) during a person's "high risk" period. You can proactively suggest taking other measures (e.g., eating snacks, drinking water, etc.) to reduce caffeine and alcohol. It may also propose adjustments to the use of location data (where applicable). This allows you to link advice to the actual sunrise time at the user's location. and check if the user is traveling to manage jet lag or the user's new room environment. We can provide appropriate advice to manage

[0307] Additionally, in some cases, SmD displays sunrise, sunset, and other sunsets with different color schemes depending on the time of day. Different background images are extracted and displayed to simulate sunset, daytime, nighttime, etc. The system can also provide users with a simulation of various time zones. The display of previous sleep records can also be adjusted to show the duration of the trip.

[0308] Software-Example Data Storage Model As previously mentioned, the system stores sleep analysis and management data. The data is accessed by the SmD and / or cloud system server(s) 3004. The information may be included in one or more databases, such as a publicly accessible database. 6 shows an example data storage model for some of the data in the present system. For example, the data may include user information 4602 such as user identification, name, address, etc. This includes the user's sleep session information 4604 (e.g., sleep data from one or more nights). sleep patterns, hypnograms, etc.), user survey responses 4603, and user addresses device items 4605 and the user's profile 4606 (e.g., age, gender, etc.) The database can function as a database of recorded environmental information 4607, sleep Associating event information 4608 and sleep location information 4609 with sleep session information Location information 4609 may be associated with user profile information. Other data models and organizations may also be implemented.

[0309] Software - Example Embodiment - Mind Clear As mentioned above, this system uses SmD processors and other devices to execute the Mind Clear Process. An example of such a process can be implemented. This "mind clearing" process helps you relax to help you fall asleep. The present invention can assist users in achieving and maintaining good posture and peace of mind.

[0310] This process allows the user to focus on any thoughts or thoughts that arise while resting. Dictating, writing down, or otherwise recording thoughts (e.g., into a digital recorder) This allows for the removal of thoughts that, if not removed, could keep the user awake. In the morning, the user accesses their records. You can browse and access your recorded thoughts or feelings. In principle, the record may be sent to the user's email box or phone message box. can be done.

[0311] This recording process is designed to minimize any disruption to the user's rest routine. For example, the use of audio recordings can allow a user to easily identify the location of a light in the dark. Finding a switch, turning on a light, finding a pen, or This system makes it possible to avoid all interference associated with accessing the data. and minimize visual distractions introduced by bright lights, allowing users to return to sleep after recording. In addition to this, the Mind Clearing feature can greatly assist in It can be voice activated (using Although recording features are available on some smartphones, This allows for phone handling and navigation through the phone menus. This may require a user to perform a voice activation, again exposing the user to interruptions and light. The enhanced mind clearing function can help to avoid such interference. .

[0312] With reduced distractions, users can record multiple "notes" to themselves. Users can then reply to these notes and You can listen to the notes. You can access these "notes" at any time. The system will perform voice recognition for delivery to the user via email or text message. It can also convert audio notes into text using the knowledge.

[0313] A flow chart of one exemplary process is shown in Figure 47. The user enters 4702 In this case, the process is started using SmD and audio recording is performed in 4704. This record can be made at any time in 4706 using an SmD or other device of this system. Optionally, you can also use a mobile phone or online server. A reply message can be sent to the remote device. The message is converted to a text format using SmD or a server in 4708. This text can be displayed on the screen of an SmD or similar application. Optionally, in 4710, the message or the converted message is The data can be sent to a remote device such as a mobile phone or an online server. These text notes / messages can be edited, saved, or deleted by the user. do.

[0314] To this end, any one or more processors of the system may provide the user with: It can be configured to perform any one or more of the following: Enter input text or record voice notes / memos, edit text notes, voice memos and text note deletion, browsing and navigating voice memos and text notes access to voice memos at any time, text readings, and other forms of communication; Access, email, SMS, and AirDrop / Bluetooth (登録商標) of Share notes via voice, activate them, or convert audio to text notes Or it can be configured to perform multiple functions.

[0315] In summary, the process is performed when the user finds it difficult to fall asleep or when the user is having difficulty falling asleep at night. It can allow you to catch any persistent thoughts if you wake up in between. The peace of mind of knowing that the user has recorded or "logged" their thoughts / worries This helps clear your mind of your own mental activity and helps you fall asleep.

[0316] Software - Example Embodiment - Nap Aid As mentioned above, this system uses the SmD processor to execute the nap assistance process. This process can be performed while the user is taking a nap (here, the expression "nap" is used to mean Typically, in addition to a long period of sleep during the night, there is a separate, relatively short period of daytime sleep. This process option can help users to (and perhaps specify this process option on days suitable for naps.) The user's nighttime sleep time and wake-up time from nap time are calculated based on the time spent going to bed and sleeping for a nap. This data is then logged by the system, including the time of waking up from the start time. By processing the data and / or nap data, the optimal nap time is calculated. If so, a processor-generated morning notification will make it easier to incorporate this into the user's daily routine. This will then act as a reminder before nap time. Another short notice follows.

[0317] When the user is at home, a dedicated unit can act as a nap monitor. This is important because the difference between a good nap and a bad one is all about timing.10 A nap of 10 to 45 minutes in any location is good, and a 90-minute nap is very good. However, arousals between 45 and 90 minutes may awaken someone in slow wave sleep. There is sexual desire and the person feels tired upon awakening.

[0318] When a user lies down to take a nap, the system detects the person's fall asleep and of course the desired duration of the nap. A nap wake-up alarm can be automatically set depending on the duration.

[0319] Such a system can implement multiple "smart data" points.

[0320] The system determines the best time for the user to start a nap (e.g., 2:30 pm) for that day. The wake-up time can be predicted based on the wake-up time of the nap. This data is used to determine the optimal time to "start the clock" and wake up. To find the optimal time, we first determine when the user actually fell asleep during their nap. This system uses a time (hypothetical time) delayed from the user's morning / wake-up time. It will advise you on the best time to take a nap by selecting a sleep delay. This value is based on the known circadian rhythm of humans and is taken as a fixed population mean (e.g., 6 This value can then be used to calculate the measured nap duration and nap duration. The system may adjust based on sleep onset latency. For example, the system may: We initially propose a 6-hour offset from wake-up time, but we have measured that the sleep onset latency is 20 minutes. If so, increase the nap delay value to 6.5 hours. When the sleep time (0-45 minutes) is obtained, the data from the sensor is also used to determine the user's state of slow wave sleep. If so, the user is refreshed from their nap. The device wakes the user up via an alarm so that they can wake up in the morning.

[0321] Reminders / schedules are based on data collected via sensors related to wake-up time. can be determined by the processor from

[0322] Software - Example Embodiment - Setup Optimizer In some versions, the system is set up using SmD processors etc. The setup optimizer process can be implemented. It has two parts: setup guide and advice feedback setup. This setup can have a graphic user interface, It may have a screen with a static image and may not require a flow of data For example, the user can swipe or click on the screen. A set of images showing the setup can be presented and the user can initially You can scroll through the sign-on options using the It may be accessible from an "About" page or a "Settings" menu, etc.

[0323] In some versions, the system or device may be configured to: In this case, it is possible to detect that the system or device is not properly positioned. This triggers a setup process to detect if the user's device is misplaced. Send notifications to users, such as by sending advice nuggets that alert users to Advice nuggets can optionally help you position your device correctly. A link to a video showing how to do this can be provided.

[0324] Such nugget feedback of the setup with this system is done as follows: It is possible to do so. (1) The user sleeps and data is supplied from the bed to the SmD in the normal way. (2) The RM20 process analyzes these parameters in the form of "sleep summary data" and signal quality. Generate a scale. (3) Sleep summary data is uploaded to a cloud server (e.g., a backend server). will be loaded. (4) The advice engine analyzes the results and, based on its logic, provides advice Send a push notification for a problem (e.g., insufficient measurement signal - relocate device) or not transmit. (5) This notification can be communicated to the phone over the network. (6) The phone will receive this notification, which contains the user's unique identifier and a link to the advice. Believe. (7) The user clicks on the notification, triggering the SmD processor to Download and view the app.

[0325] In some cases, the system implements / calculates a metric called "signal quality." This is the average data signal quality calculated throughout the sleep session. It can be an average version.

[0326] In one embodiment, the signal quality can take values of 1, 2, 3, 4, 5 (considered). For this particular scale, the midpoint "3" represents the ideal, while "2" and A "4" is acceptable quality, while "1" and "5" indicate poor signal quality.

[0327] A value of "1" indicates that the user is too far away from the sensor, resulting in a consistent image of good quality. It is shown that the respiration rate cannot be detected, i.e. the detected overall signal has an amplitude is small and / or the detected cardiopulmonary signal(s) are of very poor quality For example, in "1", the signal-to-noise ratio is very small, so the smallness of the respiratory waveform is not apparent. It is very difficult to detect significant changes.

[0328] At the other end of the spectrum, a setting of 5 means that soft clipping is very much detected on the signal. This shows that a very large signal is detected (consistently) in the subject (human, animal). This indicates that a person (e.g., a person sleeping too close to the sensor) is sleeping. The pulsating pulses can potentially skew cardiopulmonary readings (e.g., clipping respiratory peaks). (capping), masking possible apneic / hypopneic behavior and preventing extraneous movements from being triggered This means that subtle differences in the signal may be lost due to the If it is "1" or "5", the user can adjust the position of the device to get a better quality signal. It is suggested to adjust the position.

[0329] The system also returns the percentage of the total signal that falls within each bin, e.g., 62. 7% can be in bin "3", 10.54% can be in bin "2", and the remaining can be the other three bins, with the maximum classification being "3". The signal quality metric is returned.

[0330] Software - Example Embodiment - Lucid Dreaming Aid In some versions, the system uses SmD processors and other lucid dreaming aids. The Webster definition of clarity is: "Clarity of thought or style" and "presenting facts directly and A presumed capacity to perceive the truth directly The concept of lucid dreaming was first described in 1913 by Frederik van E It is produced by eden and refers to the awareness of the fact that you are dreaming. In other words, lucid dreaming recognize that a person dreams and has a level of control over their actions in the dream. The scientific consensus on lucid dreaming is that lucid dreaming is rare. Although there are some limitations, it is a healthy state of sleep that can be trained” (Dresler et al. 2011 p. 1; LaB Snyder and Gackenbach (1988, p. 230) estimate that approximately 58% of the population will experience a life-threatening condition. 10% had experienced a lucid dream at least once, and 21% reported having one or more lucid dreams per month The first book to recognize the scientific potential of lucid dreaming was by Celia Gree The first paper reviewed by experts in the field was Stumpf's (1968) study of lucid dreaming. It was published by Stephen LaBerge (1980) at the University of Kentucky. He developed lucid dreaming techniques as part of his doctoral thesis. In the 1980s, By using eye movement signals, the brain is able to consciously recognize that it is in a dream state. Researchers were able to demonstrate this, providing further scientific evidence confirming the existence of lucid dreaming. (LaBerge, 1990). Dresler et al. (2011) used lucid dreaming to investigate the neural mechanisms underlying specific dreams. Recently, they provided the first demonstration of imaging in which subjects were able to visualize their right or left hand in a dream. When asked to clench their hands, parts of the somatosensory cortex (used for movement and sensation) They discovered that the part of the brain that can be stimulated by the ATP is activated.

[0331] Such lucid dream training process is provided by the user through the SmD or server of this system. It can be used to create courses that can be shown to you. Courses can be accessed at the user's discretion. Start the training course process The user is then presented with a small sound or soundscape that can act as a trigger while dreaming. A small burst can be selected. The user then goes to sleep (and When you try to lucid dream, the device detects at least a second round of REM. or detect a subsequent REM cycle (this may be optional, possibly at the user's discretion) (This can be a setting in the training process at your discretion.) Upon detecting this, the processor of the SmD generates a sound or soundscape (or, for example, a speaker (control its playback through the Optionally, the processor may generate light instead of or in addition to sound / soundscapes. The activation of small bursts of sound and / or light levels can be controlled by This can be an environmental setting, low enough (e.g., <25dB) not to wake the user. can be adjusted / changed by the user when setting up the process.

[0332] Further Exemplary Advice Process - Sleep Problem Training In one example, the advice engine may provide advice on sleep disorders and / or sleep-disordered breathing (SDB). It can be configured to recognize "dangerous sleep," such as sleep that may indicate a problem. The SDB pathway, such as this, separates information about abnormal breathing from information about abnormal movement. Based on the observed fragmented sleep and minimal deep sleep, A lifestyle questionnaire can be presented to the user (including respiratory stability metrics) This query is used to set up the user, provide surveys, advice, It automatically analyzes your sleep data and provides different results such as "Dangerous Sleep" or "Sleep Optimizer" Based on the categorization, route the user to the appropriate resolution logic path. It can also include goli.

[0333] Such a triage process 4802 may be discussed with reference to the flowchart of FIG. This shows the overall flow of identifying "sleep problems." The data collected by the unit in process 4801 and the response to the query By combining these, the triage process 4802 The triage process can be initiated by a server such as a server. As a result of such flow processing logic, it is possible to determine one of the following sleep states: The triage engine then guides the advice engine to the correct user process. This can result in the generation of the aforementioned advice for sleep optimization. On the other hand, the triage process is carried out when the detected data indicates dangerous sleep. The various risky sleep advice processes 480 generate risky sleep advice for users. Such detection may result in "dangerous sleep." Some of these dangerous sleep disorders can be treated with advice or reports. The characteristics may include, for example, snoring, chronic insomnia, and other problems.

[0334] In one example, the advice engine's optional triage process is a back-end The server or other cloud server can initiate the download of the report to the user. Involves sending a notification (to the SmD app or email) with a link to The user can then submit a written report ( You can request a printable web page and / or PDF. Users can then present their data in a visually interesting and informative way on the website. The system allows users to view their sleep data. Such reports can be automatically selected and sent to the user from this notification based on The triage process of one or more processors may be used to determine, for example, whether a patient is "normal sleep" or "dangerous." It can detect either "unsafe sleep" or "dangerous sleep" and generate an output for the user along with its classification. This process methodology is sometimes called the "Danger Sleep Engine" and Setup profile of user responses to risky sleep-related questions in the survey The triage process can include analyzing input from the file. Sleep indicators, i.e., sleep duration (time spent asleep), time in bed, Bedtime difference, deep sleep percentage and / or minutes, REM sleep percentage and / or minutes Any one or more of sleep duration, sleep efficiency, sleep disturbance, etc. may also be assessed. The results will be reported to a sleep clinic or sleep specialist and / or provided via a website. The sleep clinic or sleep specialist may provide a It may depend on the topic.

[0335] FIG. 49 illustrates the flow of information that may be involved in triaging risky sleep. A consumer 4902 using the technology (e.g., sleep detection monitor 4904) can Sleep problems such as falling asleep, staying asleep, waking up tired, irritability, and snoring This means that a percentage threshold, e.g., falling asleep, can be experienced at times. 40% of the time, other percentages than those listed can be used. Such information (e.g., sleep patterns) can be used to The MD system (sleep detection monitor 4904) can input and / or detect the Such input may optionally be provided via an electronic "stop bang" or other form of questionnaire. responses to, surveys, or other screening information gathering tools for sleep apnea diagnosis This monitoring stage can be thought of as the "pre-triage" phase. Users who are identified as having "risky sleep" can then be notified. , a transition from pre-triage to triage stage 4906 may occur. In the urge stage, further processing such as further queries and information can occur (e.g. , the Internet or website to guide at-risk users to their solutions. (This may lead to further information such as solutions in the solution section 4908). , which may include facilitating the user's contact with a clinic or specialist. .

[0336] Such a process can be further discussed with reference to Figure 50, where Monitoring the service 5002 (e.g., SmD and / or BeD or other server components) may comprise or be part of an advice engine as described above. The advice engine, as described in more detail herein, Based on the analysis of the collected data, a pre-triage process is carried out to assess the user's sleep patterns. Turns, trends, and / or user input into either "heavy" or "mild / moderate" sleep The problem can be categorized as mild or moderate classification advice process 5006. triggers a processing operation that generates sleep optimization advice as described above. On the other hand, the severe classification advice process 5008 is managed by the triage process server 5008. 004, etc., to optimize sleep, such as treatment of obstructive sleep apnea, snoring, and chronic insomnia. Advice for improving sleep quality and / or for sleep-disordered breathing or other sleep-related health conditions (e.g. The processing operations to generate further advice processes towards obtaining a diagnosis of risky sleep You can rig it.

[0337] Figure 51 shows the OSA / SDB process 5102, the snoring process 5103, and the chronic insomnia process An example of an advertisement when the normal user process 5104 and the normal user process 5105 are triggered is shown in FIG. This figure illustrates further actions associated with the risky sleep triage process. These represent some of the "sleep problems" the engine can detect. Once process 5100 is initiated, different This allows the user to receive appropriate advice when a sleep problem is detected. This makes it easier for patients to receive the chair and support they need.

[0338] For example, in the OSA process 5102 risk, 5109 is breathing, movements (including, for example, periodic limb movements), snoring, fragmented sleep, and / or poor breathing Problems with deep sleep are assessed or confirmed. Significant problems related to OSA are found. If so, a referral process 5110 may be initiated to facilitate contact with an SDB sleep specialist. Notification. If only minor, non-critical OSA problems are detected, the analysis in 5112 may be delayed. 5104 or normal user process 5105, Different evaluation processes can be considered.

[0339] In the snore process 5103, audio analysis (e.g., recorded snoring audio data and its synchronization with breathing patterns and / or sleep disturbances. If a mild snoring problem is confirmed in 5116, the snoring-related Related service or product advice can be triggered.

[0340] In chronic insomnia process 5104, triggered cognitive behavioral therapy (CBT) in 5120 to other queries (e.g., electronic, online, or phone-based) from other sources, such as BT queries. Based on the response data, sleep patterns can be evaluated. If so, a message can be generated to refer to an insomnia specialist for advice.

[0341] The normal user process 5105 provides the aforementioned advice on sleep optimization. Such a normal user may optionally provide, for example, positive airway pressure P Being treated for sleep apnea with an AP therapy device or CPAP device In 5122, the user of such a treatment device may include a Those with mild sleep problems due to the detection process described above (e.g., high disturbance counts) In such a case, in 5124, the user may select a more suitable treatment. Getting a device to help assess whether it can promote better sleep In order to allow further devices and / or services to be We can recommend this service.

[0342] The outputs from the various data paths are then used to generate the trend in the trend update process at 5125. As shown in 5126, the triage process The input for the diagnostic screening / assessment of any of the processes is the identified or detected sleep Respiratory disorder SDB events, Cheyne-Stokes respiration (CSR) events, periodic limb movement events, high This input can include information about low respiratory rate events. The fatigue may further include identified fatigue, such as chronic fatigue or acute fatigue as identified by the above.

[0343] Example "Danger Sleep Engine" processing: An exemplary processing methodology for a risky sleep engine can now be considered.

[0344] In one example, shown in the example of FIG. 53, one or more processors may be used to perform a risky sleep assessment method. The system of the risk sleep engine 5300 is a batch process component 530 1, a determining component 5302, and a notifying component 5303. The process component may perform any of the following steps: (1) A scheduled task is executed. (2) Finally, check the progress of the related tasks. (3) Data from the database 5305 (e.g., biokinetic data, environmental data, etc.) Access. (4) Data processing begins. (5) Add the results (processed biological movement data, processed environmental data) to the database. can. (6) Call the "Decision Engine." (7) Call "Notification." (8) Update progress records. (9) Complete.

[0345] The decision engine process component 5302 performs any of the following steps: It is possible. (1) Data in the database (e.g., hypnogram(s), questionnaires) User parameters (demographics), processed bio-kinetic data, processed access to the data (e.g., environmental data, etc.) (2) Apply a probabilistic model to estimate the probability of "dangerous sleep" using the accessed data. and (3) Update the database with the results.

[0346] The notification process component 5303 may involve any of the following steps: Cut. (1) Check the user notification flag in the "Risk Sleep Table" in the database. (2) Notification services (e.g., Apple / Google notifications / push notifications to your phone) notification (such as identifying that a new sleep report is available and / or sending the report) to install and (3) Electronic Message Services 5308 (e.g., providing grid services to users) Email and / or push notification via email (when new sleep reports are available) (Identifying the cause or sending a report)

[0347] An exemplary estimation model of risky sleep for the decision engine process component is shown in the table below and in FIG. 5. This can be considered with reference to the flowchart shown in 2. Generally, the classification of risky sleep is as follows: Can be based on multiple data inputs, such as a pre-sleep profile and a user profile This can include data input from questionnaires, sleep score results, as well as batch processes. The decision engine analyzes stored user data, applies probabilistic models, and determines risk of sleep deprivation. The decision engine then updates the user database. Flags set in the base can be created in the risky sleep table and can be used in various ways. For example, the flag can be triggered by a push notification or an electronic Email communication can be initiated.

[0348] The following risky sleep table shows example sleep information ( The parameters or features 5201 are the population and user can be adjusted for region, and / or sex, and / or age. Survey data, demographics, and other factors not included in this example are also included in the analysis. For each feature, a low risk (value of "0") and a medium risk (value of "0.5") can be included. Two "bands" are implemented as the value of the bands. Areas outside these bands are considered high risk. In addition, the weighting component 5202 A coefficient (multiplier) can be applied (e.g., the weight o...

Claims

1. a speaker that plays the sound of the sound file; a processor coupled to the speaker, the processor transmitting the sound file through the speaker; a program configured to repeatedly play the sound file and repeatedly adjust the period of the sound file; The processor and Equipped with The sound file includes an exhalation cue portion and an inhalation cue portion, The inhalation cue portion is the entirety of the repeated playback and repeated adjustment of the sound file. A device that induces relaxation in the user at a fixed ratio throughout.

2. 2. The device of claim 1, wherein the ratio of the expiratory cue to the inhalation cue is 1 to 1.

4. 。

3. The repeated playback and the repeated adjustment of the sound file may be performed during a first playback period. and first playing the sound file using the sound file set to a first time length during and thereafter increasing the first length of time of said file to a second, longer length of time. and repeating the sound file using the second, longer duration during a second playback period. and regenerating the image.

4. The device may record the sound file until the adjustment of the duration of the sound file meets a threshold. Any one of claims 1 to 3, configured to be repeatedly played and repeatedly adjusted. The device described in

5. The apparatus of claim 4 , wherein the threshold comprises a minimum threshold of repetitions per minute.

6. The processor further adjusts the duration of the sound file after the adjustment satisfies the threshold. Gradually reducing the volume of the sound file being played through the speaker for a period of time.

6. The apparatus of claim 4 or 5, further configured to:

7. a motion sensor, the processor determining a measure of respiration using the motion sensor; setting the duration of the sound file as a function of the determined respiration measure; The apparatus of any one of claims 1 to 6, further configured to:

8. The processor may include a step of: , setting the duration of the sound file once as a function of the respiratory measure; The repeated adjustment of the duration of the sound file is performed by adjusting the duration of the sound file by a fixed predetermined increment. The apparatus of claim 7 , including adjusting the duration of a sound file.

9. The processor uses the motion sensor to determine a measure of the user's sleep or wakefulness. The processor may further be configured to: If sleep is detected, the music played through the speaker is played for a further first period. gradually reducing the volume of the sound file being played; If an arousal is detected, the volume reduction may be delayed or further reduced. During a second period of time, the volume of the sound file being played through the speaker is gradually increased. wherein the further second period is different from the further first period. To reduce and 9. The apparatus of claim 7 or 8, further configured to:

10. Each adjustment of the duration of the sound file may change the pitch of any sound in the sound file to substantially The device according to any one of claims 1 to 9, wherein the device maintains a constant temperature.

11. 1. A method of a processor of a device for inducing relaxation in a user, comprising: A processor is used to repeatedly play a sound file through a speaker and repeatedly adjusting the duration of the file; Including, The sound file includes an exhalation cue portion and an inhalation cue portion, The inhalation cue portion is the entirety of the repeated playback and repeated adjustment of the sound file. A method that is at a fixed ratio throughout.

12. 12. The method of claim 11, wherein the ratio of the exhalation cue to the inhalation cue is 1 to 1.

4. Law.

13. The repeated playback and the repeated adjustment of the sound file may be performed during a first playback period. and first playing the sound file using the sound file set to a first time length during and thereafter increasing the first length of time of said file to a second, longer length of time. and repeating the sound file using the second, longer duration during a second playback period. and regenerating the resulting mixture.

14. The processor continues to adjust the duration of the sound file until the adjustment satisfies a threshold. The method according to any one of claims 11 to 13, wherein the file is repeatedly played back and repeatedly adjusted. The method described.

15. The method of claim 14 , wherein the threshold comprises a minimum threshold of repetitions per minute.

16. The processor further adjusts the duration of the sound file after the adjustment satisfies the threshold. Gradually reducing the volume of the sound file being played through the speaker for a period of time. The method according to claim 14 or 15,

17. The processor further determines a measure of respiration using a motion sensor, and the processor 17. Setting the duration of the sound file as a function of the determined respiration measure.

10. The method according to any one of claims 1 to 9.

18. The processor may include a step of: , setting the duration of the sound file once as a function of the respiratory measure, The repeated adjustment of the period of the sound file is performed by adjusting the period of the sound file by a fixed predetermined increment.

18. The method of claim 17, including adjusting the time period.

19. The processor determines a measure of the user's sleep or wakefulness using a motion sensor; The processor If sleep is detected, the music played through the speaker is played for a further first period. Gradually reduce the volume of the sound file being played, If an awakening is detected, the audio is played through the speaker for a further second period. Gradually reduce the volume of the sound file being played or 18. The method of claim 17, wherein the second further period is different from the first further period.

18. The method according to claim 18.

20. Each adjustment of the period of the sound file maintains the pitch of any sound in the sound file. The method according to any one of claims 11 to 19.

21. 1. A device for promoting sleep in a user, comprising: a microphone for detecting the user's voice; a signal indicative of the user's movement generated by a sensor coupled to the microphone; a processor configured to receive the received signal, and detecting sleep information from the signal, wherein the processor Upon receiving the voice message, the voice message is recorded and the voice message is and further configured to store message data in a memory coupled to the processor. processor and Equipped with This allows the user to relax and focus on their thoughts in a way that removes their mental activity and promotes sleep. A device capable of recording.

22. The processor plays the recorded audio message using a speaker of the device.

22. The apparatus of claim 21, further configured to generate

23. The processor controls the conversion of the audio message into a text message, The method is further configured to store the text message as data in the memory.

23. The device according to claim 21 or 22.

24. The processor is configured to initiate forwarding of the text message to the user.

24. The device of claim 23, wherein

25. 25. The apparatus of claim 24, wherein the forwarding comprises an SMS or email communication.

26. The activation signal includes a voice activation signal, whereby the processor using the microphone to receive the user's voice command that initiates the voice recording process.

26. The device according to claim 21, wherein the device detects a signal.

27. 1. A method of a processor for promoting sleep in a user, comprising: A processor is used to analyze signals from the motion sensor to detect sleep information from the signals. And, Using the processor, upon receiving an activation signal, The audio message is recorded by a microphone and the audio message data is stored in the program. storing the data in a memory coupled to the processor; Including, This allows the user to relax and focus on their thoughts in a way that removes their mental activity and promotes sleep. A method for recording

28. Using the processor, play the recorded audio message through a speaker.

28. The method of claim 27, further comprising:

29. using a processor to control the conversion of the audio message into a text message; 28. The method of claim 27, further comprising storing the text message as data in the memory. Or the method according to 28.

30. using a processor to initiate forwarding of the text message to the user.

30. The method of claim 29, further comprising:

31. 31. The method of claim 30, wherein the forwarding comprises an SMS or email communication.

32. The activation signal includes a voice activation signal, whereby the processor using the microphone to receive the user's voice command that initiates the voice recording process. The method according to any one of claims 27 to 31, wherein the method detects a marker.

33. 1. A device for promoting sleep in a user, comprising: an alarm device for generating an alarm to wake a user; 1. A processor, comprising: Prompting a user to input a wake-up time and a wake-up time window, The wake-up time window ends at the wake-up time. receiving a signal from a motion sensor, the signal indicative of motion of the user; To believe and detecting sleep information using analysis of the received signals indicative of said movement; The wake-up window and the wake-up time are calculated based on the sleep information. triggering activation of an alarm device, said function and sequence of said sleep information; The function of the wake-up window and the wake-up time is and detecting a light sleep stage during the a processor configured to: An apparatus comprising:

34. The function of the sleep information is a function of the sleep time for at least a certain length of time or a certain number of episodes.

34. The apparatus of claim 33, further comprising being in a light sleep stage during sleep.

35. 34. The method of claim 33, wherein the function of the sleep information further comprises meeting a minimum amount of total sleep time. Or the device described in 34.

36. The processor may include a processor configured to randomize activation of the alarm. and further configured to trigger activation of the alarm device using a rate function. The device according to any one of claims 33 to 35,

37. The processor, upon detecting the absence of the user during the wake-up window, Claims 33-3, further configured to trigger activation of an alarm device 7. The device according to any one of claims 6 to 6.

38. The processor, upon detecting the user's wakefulness during the wake-up window, 34. The method of claim 33, further configured to trigger activation of the alarm device.

38. The device described in any one of claims 1 to 37.

39. The alarm device may be any one of an audible alarm and a visible light alarm. Apparatus according to any one of claims 33 to 38, configured to generate a plurality of

40. The function of the wake-up window and the wake-up time is a function of the current time and the wake-up window. and a plurality of comparisons with the wake-up time, The device according to any one of claims 33 to 39, which ensures that said alarm is triggered. Place.

41. 1. A method of a processor for promoting sleep in a user, comprising: Using a processor coupled to the motion sensor, Prompting a user to input a wake-up time and a wake-up time window, The wake-up time window ends at the wake-up time. receiving a signal from a motion sensor, the signal indicative of motion of the user; To believe and detecting sleep information using analysis of the received signals indicative of said movement; alarm as a function of the sleep information and as a function of the wake-up window and the wake-up time triggering activation of the system device; Including, The function of the sleep information and the function of the wake-up window and the wake-up time are detecting that the user is in a light sleep stage during the wake-up window. method.

42. The function of the sleep information is a function of whether the subject is in a light sleep stage for at least a certain length of time.

42. The method of claim 41, further comprising:

43. 42. The method of claim 41, wherein the function of the sleep information further comprises meeting a minimum amount of total sleep time. Or the method according to claim 42.

44. The processor uses a probability function to randomize activation of the alarm. Triggering activation of an alarm device as claimed in any one of claims 41 to 43. How to do it.

45. The processor may be configured to trigger the alarm upon detecting the absence of the user during the wake-up window.

45. The method of claim 41, further comprising: evaluating whether to trigger activation of a system device.

2. The method according to claim 1.

46. The processor detects a wakefulness state of the user during the wake-up window.

46. The method according to claim 41, further comprising: evaluating whether to trigger activation of said alarm device. The method according to any one of claims 1 to 4.

47. The alarm device may be any one of an audible alarm and a visible light alarm.

47. The method of any one of claims 41 to 46, wherein a plurality of

48. The function of the wake-up window and the wake-up time is a function of the current time and the wake-up window. and a plurality of comparisons with the wake-up time, A method according to any one of claims 41 to 47, ensuring that the alarm is triggered. Law.

49. 1. A device for promoting sleep in a user, comprising: adapted to access measurement data representing user movements detected by a motion sensor; a processor for processing the measurement data and generating features derived from the measurement data; a processor configured to determine a sleep factor having: Based on the determined sleep factors, a sleep score indicator, a mental recharge indicator, and and one or more indicators, including a body recharge indicator. the processor further configured to: a display for displaying said one or more indicators; An apparatus comprising:

50. The processor controls the display of the sleep score and the sleep score is based on The sleep factors include total sleep time, deep sleep time, REM sleep time and light sleep time, and wakefulness.

50. The apparatus of claim 49, comprising two or more of a wake-up time and a sleep-onset time.

51. 51. The method according to claim 49 or 50, wherein the features include time domain statistics and / or frequency domain statistics. The device.

52. The sleep score includes a sum having a plurality of component values, each component value representing a measured sleep function.

52. The sleep factor calculation method according to claim 49, wherein the sleep factor is calculated using a function of a sleep factor and a predetermined standard value of the sleep factor.

10. The device according to claim 9, wherein

53. The function includes a weighted variable that varies from 0 to 1, and the weights are set to the predetermined standard value.

53. The apparatus of claim 52, wherein the multiplication is performed.

54. The function of at least one sleep factor for determining a component value is a function of the measured sleep factor.

54. The apparatus of claim 53, wherein the coefficient is an increasing function of the coefficient.

55. The at least one sleep factor may include total sleep time, deep sleep time, REM sleep time, and 55. The apparatus of claim 54, wherein the period of sleep is one of a period of sleep and a period of light sleep.

56. The function of at least one sleep factor for determining a component value is a function of the measured sleep factor.

54. The apparatus of claim 53, wherein the eigenvalue is an initially increasing and then decreasing function of the eigenvalue.

57. 57. The apparatus of claim 56, wherein the at least one sleep factor is REM sleep time. 。

58. The function of at least one sleep factor for determining a component value is a function of the measured sleep factor.

54. The apparatus of claim 53, wherein the coefficient is a decreasing function of the coefficient.

59. The at least one sleep factor is one of a time to fall asleep and a time to wake up during sleep.

59. The apparatus of claim 58.

60. 49-5. The display of the sleep score includes displaying a total sleep score.

10. The device according to any one of claims 9.

61. The display of the sleep score includes displaying a graphical pie chart, The graph of the pie is divided into segments around its periphery, and each segment around the periphery The segment size is due to the predetermined standard value of each sleep factor, and each segment has its own a function of the measured sleep factors and the predetermined standard values of the respective sleep factors; 61. The device of any one of claims 49 to 60, wherein the device is radially filled with

62. The predetermined standard value for total sleep time is 40, the predetermined standard value for deep sleep time is 20, and R The predetermined standard value of the EM sleep time is 20, the predetermined standard value of the light sleep time is 5, and The predetermined standard value for wakefulness time is 10, and / or the predetermined standard value for sleep onset is 5. Item 62. The device according to any one of items 49 to 61.

63. The processor accesses detected ambient parameters including ambient light and / or sound. and further configured to adjust settings of the device during at least some operation of the device. and the settings to be adjusted include screen brightness and / or volume.

10. The device described in claim 1.

64. The processor controls the display of the mental recharge indicator, 64. The method of claim 49, wherein the indicator is based on REM sleep time. Device.

65. The mental recharge indicator comprises a REM sleep factor and a predetermined value of the REM sleep factor.

65. The apparatus of claim 64, comprising a function of a standard value of

66. The function of the REM sleep factor and the predetermined standard value of the sleep factor is 66. The apparatus of claim 65, comprising an increasing and decreasing function of time.

67. The mental recharge indicator correlates the measured REM sleep time with the standard REM sleep time. It is displayed as a graphic indicator relating the The indicator is a segmented display that is filled proportionally according to the percentage.

67. A device according to any one of claims 64 to 66, having the appearance of a battery.

68. The processor controls the display of the body recharge indicator and Apparatus according to any one of claims 49 to 67, wherein the indicator is based on deep sleep time. 。

69. The body recharge indicator comprises a deep sleep factor and a predetermined standard value of the deep sleep factor.

69. The apparatus of claim 68, comprising a function of:

70. The function of the deep sleep factor and the predetermined standard value of the deep sleep factor is 70. The apparatus of claim 69, comprising an increasing function.

71. The body recharge indicator may be configured to divide the measured deep sleep time by a predetermined standard deep sleep time. It is displayed as a graphic indicator relating the percentage The indicator is a segmented bar that is filled proportionally according to the percentage.

71. A device according to any one of claims 68 to 70, having the appearance of a battery.

72. adapted to access measurement data representing user movements detected by a motion sensor; 1. A method for promoting sleep using a processor comprising: processing the measurement data; and determining sleep factors having characteristics derived from the measurement data. To ask for and Based on the determined sleep factors, a sleep score indicator, a mental recharge indicator, and generating one or more indicators, including a body recharge indicator, and, controlling the display of the one or more indicators; A method comprising:

73. The display includes the sleep score, and the sleep factors on which the sleep score is based are , total sleep time, deep sleep time, REM sleep time and light sleep time, waking time and time to fall asleep 73. The method of claim 72, comprising two or more of:

74. 74. The method of claim 72 or 73, wherein the features include time domain statistics and frequency domain statistics. Law.

75. The sleep score includes an aggregate having a plurality of component values, each component value being a sleep factor and a sleep score. Any of claims 72 to 74, wherein the sleep factor is determined using a function of a predetermined standard value of the sleep factor.

2. The method according to claim 1.

76. The function includes a weighted variable that varies from 0 to 1, and the weights are set to the predetermined standard value.

76. The method of claim 75, wherein the multiplication is performed.

77. 4. The method of claim 3, wherein the function of at least one sleep factor for determining a component value is an increasing function.

76. The method according to claim 76.

78. The at least one sleep factor may include total sleep time, deep sleep time, REM sleep time, and 78. The method of claim 77, wherein the period of sleep is one of: sleep time and light sleep time.

79. The function of at least one sleep factor for determining a component value is an increasing-decreasing function.

77. The method of claim 76.

80. 80. The method of claim 79, wherein the at least one sleep factor is REM sleep time. 。

81. 4. The method of claim 1, wherein the function of at least one sleep factor for determining a component value is a decreasing function.

76. The method according to claim 76.

82. The at least one sleep factor is one of a time to fall asleep and a time to wake up during sleep.

82. The method of claim 81 .

83. 73. The method of claim 72, wherein displaying the sleep score includes displaying an aggregate sleep score.

83. The method according to any one of claims 1 to 82.

84. Displaying the sleep score includes displaying a graphical pie chart, The graphic pie chart is divided into segments around its periphery, and each segment around the periphery The segment size is attributed to a predetermined standard value of each sleep factor, and each segment is radially filled in accordance with a function of the sleep factors and the predetermined standard values of the sleep factors. The method according to any one of claims 72 to 83.

85. The predetermined standard value for total sleep time is 40, the predetermined standard value for deep sleep time is 20, and R The predetermined standard value of the EM sleep time is 20, the predetermined standard value of the light sleep time is 5, and The predetermined standard value for wakefulness time is 10, and / or the predetermined standard value for sleep onset is 5. Item 72 to 84. The method according to any one of items 72 to 84.

86. The display includes the mental recharge indicator, the mental recharge indicator being a measure of 86. The method of any one of claims 72 to 85, wherein the method is based on estimated REM sleep time.

87. The mental recharge indicator is a function of the measured REM sleep factor and the REM sleep factor.

87. The method of claim 86, wherein the kinematic coefficient is determined as a function of predetermined standard values of kta.

88. The function of the REM sleep factor and a predetermined standard value of the sleep factor is 88. The method of claim 87, wherein the function includes an initial increase and then a decrease in the selected REM sleep time. method.

89. The mental recharge indicator correlates the measured REM sleep time with the standard REM sleep time. - a graphic indicator relating the The battery is a segmented battery that is filled proportionally according to the percentages.

89. The method of any one of claims 86 to 88, having the appearance of:

90. The display includes the body recharge indicator, the body recharge indicator being 90. The method of any one of claims 72 to 89, wherein the method is based on a measured deep sleep time.

91. The body recharge indicator is based on a measured deep sleep factor and the location of the deep sleep factor.

91. The method of claim 90, wherein the value is determined as a function of a certain standard value.

92. The function of the deep sleep factor and the predetermined standard value of the deep sleep factor is 92. The method of claim 91, comprising an increasing function.

93. The body recharge indicator may be configured to divide the measured deep sleep time by a predetermined standard deep sleep time. A graphic indicator relating the The data is stored in a segmented battery that is filled proportionally according to the percentage.

93. The method of any one of claims 90 to 92, having the appearance of

94. accessing measurement data representative of user movements detected by a movement sensor; The measurement data is processed to obtain sleep factors having characteristics derived from the measurement data. and accessing sensed environmental condition data from one or more environmental sensors; generating and displaying a sleep hypnogram, the sleep hypnogram comprising: generating and displaying a plot of sleep stages over time during the session; 1. A sleep-promoting device comprising: one or more processors configured to:

95. The sleep hypnogram is temporally associated with sleep stages or transitions between sleep stages.

95. The method of claim 94, further comprising: plotting at least one detected environmental condition over a predetermined time period. Device.

96. The detected environmental condition includes any one of a light event, a sound event, and a temperature event.

96. The device according to claim 94 or 95.

97. 94-96, wherein the detected environmental conditions include an event corresponding to a detected sleep disturbance.

96. The device according to any one of claims 96 to 96.

98. 98. The apparatus of claim 97, wherein the detected sleep disturbance comprises a period of awakening without sleep.

99. The motion sensor and / or the one or more environmental sensors may further comprise: a sensor coupled to said processor for transmitting data representing the detected signal from said sensor to said processor; 99. The apparatus of any one of claims 94 to 98,

100. receiving measurement data from a motion sensor representative of a user's motion; processing the measurement data to estimate sleep factors using features derived from the measurement data; and accessing sensed environmental condition data from one or more environmental sensors; generating a sleep hypnogram, the sleep hypnogram comprising a sequence of a sleep session; generating a plot of sleep stages over time; controlling a display presenting said sleep hypnogram; A processor method for promoting sleep, comprising:

101. The detected environmental condition information is then stored in the hypnogram in a time-series manner in relation to sleep stages.

101. The method of claim 100, further comprising presenting the information in association with the

102. The detected environmental condition includes any one of a light event, a sound event, and a temperature event.

102. The method of claim 100 or 101.

103. 100. The system of claim 100, wherein the detected environmental condition includes an event corresponding to a detected sleep disturbance.

103. The method according to any one of claims 1 to 102.

104. 104. The method of claim 103, wherein the detected sleep disturbance comprises a period of awakening without sleep.

105. Detecting the user's movements using the motion sensor and / or detecting the one or more environmental The method of any one of claims 100 to 104, further comprising detecting the environmental condition using an environmental sensor. The method according to any one of claims 1 to 10.

106. The display and a processor coupled to the display, the processor detecting a motion of the display by a motion sensor; and configured to access measurement data representative of the user's movements detected by the process. The sleep processor processes the measurement data to generate a sleep profile having features derived from the measurement data. The processor is configured to determine factors such as daily caffeine consumption, daily alcohol intake, and The user can select one or more of the following: daily energy consumption, daily stress level, and daily exercise amount. a processor further configured to prompt input of a user parameter; One or more determined sleep factors and one of the input user parameters. and further configured to display a temporal correlation of the plurality of sleep sessions between one or more of the plurality of sleep sessions. the processor; A sleep-promoting device comprising:

107. The processor may further include a processor for processing the one or more sleep factors and the one or more input and guiding the user to select user parameters for the display.

107. The device of claim 106.

108. one of the determined sleep factors includes a total sleep time of a sleep session; The device according to claim 106 or 107.

109. The processor may further include a processor for combining one or more determined sleep factors with the user's location. ambient sound level, ambient light level, ambient temperature level, ambient air pollution level, and and detecting multiple sleep sessions with environmental data representing one or more ambient sleep conditions, including weather conditions.

108. The method of claim 106 or 107, further configured to display a temporal correlation of the Equipment.

110. The processor accesses weather data based on the detection of the location of the device.

110. The apparatus of claim 109, further configured to:

111. The device may also include one or more determined sleep factors and one or more input user inputs. and the user parameters, the ambient sound level, the ambient light level, the ambient one or more ambient sleep conditions, including ambient temperature level, ambient air pollution level, and weather conditions; generating the temporal correlation of the plurality of sleep sessions between the plurality of sleep sessions, The device of any one of claims 106 to 110.

112. 1. A processor method for promoting sleep, comprising: Using a processor, the measurement data representing the user's movements detected by the movement sensor is processed. To access and The processor is used to process the measurement data to generate a signal derived from the measurement data. determining sleep factors having characteristics; The processor is used to calculate daily caffeine consumption, daily alcohol consumption, daily Input of user parameters including one or more of stress level and daily exercise amount. Inducing Using the processor, one or more determined sleep factors and the input user and determining a temporal correlation between one or more of the sleep parameters across multiple sleep sessions. Display on the screen and A method comprising:

113. Using the processor, the one or more input user parameters are 12. The method of claim 11, further comprising prompting the user to select for display of the correlation.

2. The method according to claim 2.

114. 112 or 113, wherein the determined sleep factors include a total sleep time of a sleep session.

113.

115. one or more determined sleep factors and the input user parameters. and one or more of an ambient sound level, an ambient light level, an ambient with one or more ambient sleep conditions, including temperature levels, ambient air pollution levels, and weather conditions; and generating the temporal correlation of the plurality of sleep sessions between the plurality of sleep sessions.

114. The method of any one of claims 114 to 114.

116. Access measured sleep data representing the user's movements detected by the motion sensor and processing the measured sleep data to generate a sleep pattern having characteristics derived from the measured sleep data. Finding the factors, accessing measured environmental data representative of ambient sleep conditions; Prompting the user to input lifestyle data for each sleep session; assessing the sleep factors to detect sleep problems; one or more processors configured to: The measured sleep data, the calculated sleep factor data, the measured environment and at least one of the input user lifestyle data. transmitting at least some of the detected sleep data and evaluating the transmitted data; It is designed to facilitate the selection of possible or most likely causes of a problem. a transmitter; receiving one or more advice messages associated with the selected cause; and a receiver configured to: a receiver including the content; a display for displaying the received one or more advice messages to a user. and, A sleep promoting system comprising:

117. The one or more advice messages may be continuously displayed upon continuous detection of the sleep problem.

117. The system of claim 116, further comprising a sequence of advice messages generated over time. Hmm.

118. The measured environmental data includes detected light, detected sound, and detected temperature.

118. The system of claim 116 or 117, comprising one or more of:

119. The sleep factors include sleep latency, REM sleep time, deep sleep time, and number of sleep interruptions. The system of any one of claims 116 to 118, comprising one or more of:

120. The detected sleep problems were excessively short REM time, excessively long REM time, Fragmented REM time, excessively short deep sleep, excessively long deep sleep and deep sleep time fragmented states. A system described in any one of claims 1 to 119.

121. The detected sleep problem is that the user's sleep contains too many interruptions. The system of any one of claims 116 to 120.

122. Evaluating the measured environmental data and the input user lifestyle data Selecting one as the most likely cause of the detected sleep problem is done by calculating the probability. The system of any one of claims 116 to 121, comprising calculating

123. 117. The method of claim 116, wherein generating the advice message includes triggering a push notification.

123. A system according to any one of claims 1 to 122.

124. The selected sleep problem associated with the received advice. The most likely cause is a further improvement based on evaluating historical sleep data to detect sleep trends. The system according to any one of claims 116 to 122.

125. The one or more processors and the receiver generate a data indicating the results of the triage process. and receiving data from the patient, the triage process being based on the detected sleep problem. and determining a probability based on the determined probability of the risky sleep state, Probability of one or more of the following: risk of apnea, risk of snoring, and risk of chronic insomnia The system of any one of claims 116 to 124, comprising calculating:

126. The one or more processors and the receiver facilitate access to a sleep health professional. and receiving a generated report having information regarding the risky sleep condition. The system of claim 125.

127. The one or more processors and the transmitter receive data indicative of a user's location. and transmitting one or more advice messages based on the transmitted location data.

127. The method according to any one of claims 116 to 126, further configured to receive a message. system.

128. 128. The method of claim 127, wherein the received advice message includes jet lag advice. system.

129. accessing measurement data representative of user movements detected by a movement sensor; processing the measurement data to generate sleep factors having characteristics derived from the measurement data; To ask for and accessing measured environmental data representative of ambient sleep conditions; Prompting the user to input lifestyle data for each sleep session; assessing the sleep factors to detect sleep problems; The following types of data are collected: the measurement data, the determined sleep factor data, the measured environmental data and the input user lifestyle data. transmitting at least some of at least one of the evaluating the data and determining a possible cause or most likely cause of the detected sleep problem; Facilitating the selection of a cause; One or more generated electronic advice messages associated with the selected cause. receiving an advice message, the advice message including advice to promote sleep; receiving, including the content; displaying the received electronic advice message; 11. A method for promoting sleep in an electronic system using one or more processors, comprising:

130. The environmental data may be one or more of detected light, detected sound, and detected temperature.

130. The method of claim 129, wherein includes a plurality.

131. The sleep factors include REM sleep time, deep sleep time, excessive sleep interruptions, REM sleep time, REM time is too short, REM time is too short or too long, REM time is too fragmented excessively short deep sleep time, excessively long deep sleep time, and 131. The method of claim 129 or 130, wherein the state includes one or more of the following fragmented states: 。

132. Evaluating the measured environmental data and the input user lifestyle data Selecting one as the most likely cause of the detected sleep problem causes includes:

132. The method of claim 129, further comprising evaluating historical sleep data to detect sleep trends. The method according to any one of claims 1 to 4.

133. and performing a triage process, the triage process including: determining a risky sleep state by calculating a probability based on the determined sleep problem; The probability of having sleep apnea, snoring, and chronic insomnia is 133. The method of any one of claims 129 to 132, comprising one or more probabilities.

134. and receiving a report indicating the results of the triage process, the report comprising:

133. The method of claim 133, comprising: providing information about the risky sleep condition to facilitate access to an expert; The method described below.

135. At least one of the one or more advice messages may be 135. The method of claim 129, wherein the detection is based on a detected change in orientation or location. The method according to any one of claims 1 to 4.

136. 136. The method of claim 135, wherein the generated advice message includes jet lag advice. How to do it.

137. One or more processors are used to represent user movements detected by the motion sensors. and / or accessing sleep factors having characteristics derived from the measurement data. and One or more processors are used to access measured environmental data representative of ambient sleep conditions. and Using one or more processors, the input user data obtained for each sleep session is access to user lifestyle data; One or more processors are used to evaluate the sleep factors to detect sleep problems. And, Using one or more processors, the measured environmental data and the input user Evaluate the user's lifestyle data to identify the most likely causes of the detected sleep problems and select one, generating one or more electronic advice messages associated with the selected one; wherein the advice message includes advice content to promote sleep. and and a method for promoting sleep using an electronic system.

138. Generating one or more advice messages includes continuously detecting the sleep problem. generating a continuous time-series of advice messages upon receiving the advice message; 37. The method according to claim 37.

139. The environmental data may be one or more of detected light, detected sound, and detected temperature. The sleep factors include a sleep latency, a REM sleep time, a deep sleep time, and a sleep 139. The method of claim 137 or 138, comprising one or more of a number of interruptions.

140. The detected sleep problems were excessively short REM time, excessively long REM time, Fragmented REM time, excessively short deep sleep, excessively long deep sleep any one or more of the following: a state of deep sleep, fragmented deep sleep, and excessive sleep interruptions 140. The method of any one of claims 137 to 139, comprising a number.

141. Evaluating the measured environmental data and the input user lifestyle data Selecting one as the most likely cause of the detected sleep problem is done by calculating the probability.

141. The method of any one of claims 137 to 140, comprising calculating

142. generating the advice message includes triggering a push notification; 142. The method of any one of claims 137 to 141.

143. 10. The method of claim 1, wherein the method is performed by one or more network server processes. 37-142. The method of any one of claims 37-142.

144. Evaluating the measured environmental data and the input user lifestyle data Selecting one as the most likely cause of the detected sleep problem causes includes:

144. The method of claim 137, further comprising evaluating historical sleep data to detect sleep trends. The method according to any one of claims 1 to 4.

145. and performing a triage process, the triage process including: determining a risky sleep state by calculating a probability based on the determined sleep problem; The probability of having sleep apnea, snoring, and chronic insomnia is 145. The method of any one of claims 137 to 144, comprising one or more probabilities.

146. The triage process facilitates access to sleep health professionals.

146. The method of claim 145, further comprising triggering the generation of a report having information regarding the state of the device.

147. The triage process generates a report based on a comparison of a threshold value with a calculated probability value.

147. The method of claim 145 or 146, wherein the method triggers.

148. based on the detected location or the detected change in location, generating at least one of the plurality of advice messages.

148. The method of any one of claims 137 to 147.

149. 149. The method of claim 148, wherein the generated advice message includes jet lag advice. How to do it.

150. Measured sleep data representing the user's movements detected by the motion sensor; and / or accessing sleep factors having characteristics derived from the measured sleep data; 、 accessing measured environmental data representative of ambient sleep conditions; Access entered user lifestyle data collected for each sleep session To do, assessing the sleep factors to detect sleep problems; the measured sleep data, the sleep factor data, the measured environmental data; and evaluating one or more of the input user lifestyle data to determine the detection result. Selecting the possible or most likely cause of the sleep problem presented; generating one or more advice messages associated with the selected cause; the advice message includes advice content to promote sleep. To do, The generated one or more advice messages are sent to a device associated with the user. transmitting the information to a display device; EP 1 199 593 A1 2 5 10 15 20 25 30 35 40 45 50 55 Description [0001] An electronic system for promoting sleep, comprising one or more processors configured to: Hmm.

151. The generated one or more advice messages are generated based on the sleep problem detected continuously.

150. The method of claim 150, further comprising: The system described in

152. Evaluating the measured environmental data and the input user lifestyle data Selecting one as the most likely cause of the detected sleep problem is done by calculating the probability.

152. The system of claim 150 or 151, comprising:

153. 150. The method of claim 150, wherein generating the advice message includes triggering a push notification. A system described in any one of claims 1 to 152.

154. Evaluating the measured environmental data and the input user lifestyle data Selecting one as the most likely cause of the detected sleep problem may include:

154. Any of claims 150 to 153, further comprising evaluating the data to detect sleep trends.

10. The system of claim 1.

155. The one or more processors are configured to perform a triage process, The triage process calculates a probability based on the detected sleep problem and identifies a dangerous sleep state. determining the probability, wherein determining the probability includes determining a risk of sleep apnea, a risk of snoring, and risk of chronic insomnia. 0-154. A system according to any one of claims 0-154.

156. The triage process facilitates access to sleep health professionals.

156. The system of claim 155, further comprising: a trigger for generating a report having information regarding the state of the device;

157. The triage process generates a report based on a comparison of a threshold value with a calculated probability value.

157. The system of claim 155 or 156, which triggers.

158. At least one of the generated one or more advice messages comprises: Claims 150 to 15 are based on the determined location and / or change of location.

8. A system according to any one of claims 7 to 7.

159. The at least one generated advice message includes jet lag advice.

159. The system of claim 158.

160. During a sleep session, we collect and store your measured sleep data, associated with your movement data. Receiving and processing the motion data to generate sleep factors having characteristics derived from the motion data; and measuring ambient sleep conditions using one or more environmental sensors; Creating a sleep record for the sleep session using sleep factors and the ambient sleep conditions And, displaying the sleep factors on a display coupled to the processor; transmitting the sleep log to a server; A system for promoting sleep, comprising: a processor configured to:

161. The processor control instructions of the processor, during execution of the auto-start process, The motion data transmitted from the sensor module is evaluated to detect detected breathing. determining the presence or absence of a user based on the quality; upon detecting the presence of the user, initiating a sleep session information collection process; 161. The system of claim 160, further controlling the processor of the device to:

162. The processor control instruction of the processor, during execution of the automatic stop process, The motion data transmitted from the sensor module is evaluated to determine the presence or absence of a user. Decline, Terminating the sleep session information collection process upon detecting sustained absence of the user; 162. The system of claim 160 or 161, further controlling the processor of the device to: Tem.

163. The detection of the sustained absence of the user includes detecting the sustained absence relative to an expected wake-up time. The system of claim 162, further comprising:

164. The sensor module further comprises a receiver for receiving a control command, and a processor for controlling the control command. updating the processor to send a termination command to the receiver of the sensor module; The system according to any one of claims 160 to 163,

165. processor control instructions for detecting environmental parameters and / or device locations; based on at least the detected environmental parameters or the location of the device; The processor of the device is configured to adjust parameters of the sleep session information collection process. The system according to any one of claims 160 to 164, configured to control a processor. Stem.

166. 165. The environmental parameters include light and / or sound settings of the device. The system described in

167. The parameters are adjusted in determining the local time at the detected location. The system of claim 165.

168. Processor control instructions control activation and deactivation of the one or more environmental sensors. the processor of the device to generate a user interface for selectively controlling the activation of the A system according to any one of claims 160 to 167, configured to control a Hmm.

169. The processor control instructions are configured to generate an alarm to remind the user to go to sleep.

169. The method of claim 160, wherein the method is configured to control the processor of the device in accordance with the 10. The system according to any one of claims 1 to 9.

170. Processor control instructions configure the device to generate said alarm upon detection of time to sleep.

170. The system of claim 169, configured to control the processor of a device.

171. 171. The system of claim 170, wherein the time to sleep is a calculated optimal nap time. Stem.

172. The one or more environmental sensors may include a humidity sensor, a sound sensor, a light sensor, and an air quality sensor. The system of any one of claims 160 to 171, comprising a sensor.

173. A method for performing a sleep session information collection process using a processor in a device It is a law, receiving motion data transmitted from the sensor module; processing the motion data to generate sleep factors having characteristics derived from the motion data; and measuring ambient sleep conditions using one or more environmental sensors; Creating a sleep record of a sleep session using sleep factors and the ambient sleep conditions and, displaying the sleep factors on a display coupled to the processor; transmitting the sleep log to a server; A method comprising:

174. executing an auto-start process with the processor; Evaluating the motion data transmitted from the sensor module to determine the detected respiration rate. determining the presence or absence of a user based on the detection quality; Upon detecting the presence of the user, initiating a sleep session information collection process. 、 174. The method of claim 173, further comprising performing the method by:

175. executing an auto-shutdown process with the processor; Evaluating the motion data transmitted from the sensor module to determine the presence or absence of a user Determining the presence of Terminating the sleep session information collection process upon detecting sustained absence of the user. and, 175. The method of claim 173 or 174, further comprising performing the method by:

176. The detection of the sustained absence of the user includes detecting the sustained absence relative to an expected wake-up time.

176. The method of claim 175, comprising determining:

177. The sensor module further comprises a receiver for receiving a control command, and the method further comprises:

18. The method of claim 17, further comprising sending a termination command to the receiver of the sensor module. 5 or 176.

178. detecting environmental parameters and / or the location of said device; and based on the detected parameters or the detected location of the device. and adjusting parameters of a session information collection process.

177. The method of any one of claims 177.

179. 179. The method of claim 178, wherein the parameters include light and / or sound settings of the device. How to post.

180. The parameters are adjusted in determining the local time at the detected location. The method of claim 179,

181. Selectively controlling activation and deactivation of the one or more environmental sensors. Any one of claims 173 to 180, further comprising generating a user interface. The method described below.

182. 10. The method of claim 1, further comprising generating an alarm to remind the user to go to sleep. 73-181. The method of any one of claims 73-181.

183. 183. The method of claim 182, wherein the alarm is generated by detecting time to sleep. The method described.

184. The time to sleep is determined by the clock time meeting the calculated optimal nap time. and the method further comprises calculating the optimal time to take a nap, Note: The optimal time to take a nap is based on processing logged wake-up times. The method of claim 183.

185. The one or more environmental sensors may include a humidity sensor, a sound sensor, a light sensor, and an air quality sensor. The method of any one of claims 173 to 184, comprising a sensor.

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