Method for utilizing sleep-wake cycle model and apparatus therefor

Abstract

The present invention relates to a method and an apparatus for measuring sleep data from a subject in an unrestrained manner, generating a sleep-wake cycle model using the measured sleep data, and further utilizing same. In particular, the present invention relates to a method and an apparatus implemented to provide various sleep guides or activity guides to a subject by utilizing a sleep-wake cycle model generated for a specific subject.

Description

Method for utilizing a sleep-wake cycle model and a device for doing so

[0001] The present invention relates to a method and apparatus for measuring sleep data from a subject without restraint, generating a sleep-wake cycle model using the measured sleep data, and further utilizing the same. In particular, the present invention relates to a method and apparatus implemented to provide various sleep guides or activity guides to a subject by utilizing a sleep-wake cycle model generated for a specific subject.

[0002] Generally, to obtain sleep information from subjects, their circadian rhythms and sleep homeostasis can be measured. However, while sleep information can be collected in research environments utilizing restraining equipment, there is a problem in that it is difficult to measure in the subjects' actual daily environments.

[0003] Accordingly, methods have recently been frequently proposed to measure sleep information by equipping a small watch-type device that causes minimal restraint to the subject and having the subject wear said device. However, there is a problem in that the watch-type device has limitations in continued use because its operating time is short due to the small battery capacity, and sleep information cannot be collected while the battery is charging, making it difficult for the subject to wear it.

[0004] In addition, subjects are required to wear a watch-type device to measure sleep information; however, as they may not wear it due to reasons such as discomfort, gaps in the collection of sleep information during the sleep period may occur.

[0005] The present invention aims to provide a method and apparatus for measuring sleep data unconditionally in a resting place such as a bed, estimating circadian rhythms and sleep homeostasis using the measured sleep data, and deriving a sleep-wake cycle model based thereon.

[0006] Meanwhile, the technical problems of the present invention are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by a person skilled in the art from the description below.

[0007] In an apparatus according to one embodiment of the present invention, a method for generating a sleep-wake cycle model using unconstrained sleep data may include: detecting vibration data and pressure data while a subject is sleeping on the bed using a sensor module attached to the bed; estimating the subject's sleep time, circadian rhythm, and sleep homeostasis over a certain period using the detected vibration data and pressure data; generating a sleep-wake cycle model by accumulating the circadian rhythm and sleep homeostasis estimated over the certain period; and outputting the generated sleep-wake cycle model to which weights set for the subject are applied.

[0008] A method according to one embodiment of the present invention may further include the action of determining the vibration data as the heart rate data of the subject, the action of binarizing a certain time interval before and after the time at which the lowest heart rate value appears in the heart rate data and the remaining time interval, and the action of determining the data including the binarized time intervals as a circadian rhythm for autonomic nervous system activity.

[0009] A method according to one embodiment of the present invention may further include the operation of determining a weight set for the subject and the operation of applying the weight to the circadian rhythm to estimate the circadian rhythm according to the weight.

[0010] A method according to one embodiment of the present invention may further include the operation of binarizing the pressure data to confirm it as bed occupancy data indicating whether the subject occupies the bed.

[0011] A method according to one embodiment of the present invention may further include the operation of setting the data from sunrise to sunset among the bed occupancy data to 1 and processing the remaining data to 0, and outputting a sleep homeostasis graph representing the value of sleep homeostasis according to time.

[0012] A method according to one embodiment of the present invention may further include the operation of increasing the value of sleep homeostasis in the section where the bed occupancy data is set to '1' in the sleep homeostasis graph to a specified value, and the operation of decreasing the value of sleep homeostasis in the section where the bed occupancy data is set to '0' in the sleep homeostasis graph to a specified value.

[0013] A method according to one embodiment of the present invention may further include the steps of classifying and verifying the sleep time into weekend sleep time and weekday sleep time, and calculating the standard required sleep time for the subject by weighted averaging the verified weekend sleep time and weekday sleep time.

[0014] A method according to one embodiment of the present invention may further include the operation of setting the value of sleep homeostasis of the sleep homeostasis graph to '0' when the detected sleep time corresponds to the reference required sleep time.

[0015] A method according to one embodiment of the present invention may further include, based on the vibration data, an operation of setting the decreasing slope of the sleep homeostasis graph to less than or equal to a set angle value when it is determined that the subject wakes up more than a specified number of times during sleep.

[0016] A method according to one embodiment of the present invention may further include the operation of identifying a first time interval during which the value of the circadian rhythm is greater than the value of sleep homeostasis, and the operation of identifying a second time interval during which the value of the circadian rhythm is smaller than the value of sleep homeostasis.

[0017] A method according to one embodiment of the present invention may further include the operation of determining that the level of arousal of the subject in the first time interval is higher than the arousal reference value, and the operation of determining that the level of arousal of the subject in the second time interval is lower than the arousal reference value.

[0018] A method according to one embodiment of the present invention may further include an operation of outputting to the sleep-wake cycle model a result of comparison with the wakefulness reference value for the first and second time intervals.

[0019] A device for generating a sleep-wake cycle model using unconstrained sleep data according to one embodiment of the present invention may include a sensor module attached to a bed and detecting vibration data and pressure data while a subject sleeps on the bed, a display, and a processor that uses the detected vibration data and pressure data to estimate the subject's sleep time, circadian rhythm, and sleep homeostasis for a certain period, accumulates the circadian rhythm and sleep homeostasis estimated for the certain period to generate a sleep-wake cycle model, and controls the display to output the generated sleep-wake cycle model to which weights set for the subject are applied.

[0020] A method according to another embodiment of the present invention is executed by a device comprising a processor, a sensor module, and a memory, and the method may be a method utilizing a sleep-wake cycle model generated based on unconstrained sleep data. In this case, the method may include: an operation of detecting vibration data and pressure data while a subject is sleeping in a bed using a sensor module; an operation of estimating the subject's sleep time, circadian rhythm, and sleep homeostasis over a certain period; an operation of generating a sleep-wake cycle model by accumulating the circadian rhythm and sleep homeostasis estimated over the certain period; and an operation of providing sleep guide information or activity guide information generated using the sleep-wake cycle model to the subject.

[0021] Additionally, in the above method, the operation of estimating the circadian rhythm of the subject may include: an operation of weighted summing sunrise-sunset binary data based on sunrise-sunset time information, bed occupancy data indicating whether the subject occupies the bed, and heart rate binary data based on the subject's heart rate data; and an operation of curve fitting the weighted summed data into a cosine waveform.

[0022] In addition, the operation of estimating the sleep homeostasis of the subject in the above method may include using bed occupancy data indicating whether the subject occupies the bed, increasing the sleep homeostasis during the time when it is determined that the subject is not occupying the bed, and decreasing the sleep homeostasis during the time when it is determined that the subject is occupying the bed.

[0023] In addition, the operation of estimating the sleep homeostasis of the subject in the above method may include the operation of setting the slope of the sleep homeostasis graph differently depending on the number of times the subject wakes up from sleep, the heart rate variability during sleep, or whether there is non-sleep activity.

[0024] In addition, the operation of providing sleep guide information or activity guide information generated using the sleep-wake cycle model to the subject in the above method may include providing information on the day's fatigue to the subject by referring to the degree of discrepancy between the subject's first circadian rhythm estimated based on data collected over the past n days excluding the day, and the second circadian rhythm estimated based on data collected up to the time the subject woke up on the day.

[0025] In addition, the operation of providing sleep guide information or activity guide information generated using the sleep-wake cycle model in the above method to the subject may include: an operation of outputting a graph of circadian rhythm and sleep homeostasis predicted after the subject's wake-up time; and an operation of displaying one of an expected sleep time, which is when the subject is expected to sleep, or an expected wake-up time, which is when the subject is expected to wake up, based on the graph.

[0026] In addition, the operation of providing sleep guide information or activity guide information generated using the sleep-wake cycle model to the subject in the above method may include: the operation of outputting a sleep homeostasis graph suitable for the subject; and the operation of displaying at least one of information on when the subject should perform the main activity, information on when the subject should go to sleep, information on when the subject should wake up, or a time period when the subject's level of arousal is expected to be high.

[0027] Additionally, a device utilizing a sleep-wake cycle model generated based on unrestrained sleep data according to another embodiment of the present invention may include: a sensor module that detects vibration data and pressure data while a subject is sleeping in a bed; and a processor that uses the detected vibration data and pressure data to estimate the subject's sleep time, circadian rhythm, and sleep homeostasis over a certain period, accumulates the circadian rhythm and sleep homeostasis estimated over the certain period to generate a sleep-wake cycle model, and generates sleep guide information or activity guide information using the sleep-wake cycle model.

[0028] Additionally, the device may further include a display that outputs the sleep guide information or activity guide information.

[0029] According to the present invention, sleep data can be collected while the subject performs normal sleep behavior without restraining the subject, and the subject's circadian rhythm and sleep homeostasis can be evaluated using the collected unrestrained sleep data.

[0030] In addition, the present invention supports daily routine scheduling decisions by identifying periods of high and low alertness during the subject's waking hours through a sleep-wake cycle model, and can suggest the necessary sleep time, time to fall asleep, and time to wake up to increase alertness during the waking hours.

[0031] In addition, the present invention can quantify the degree of discrepancy between sunrise-sunset binary data and daily rhythms, as well as the discrepancy in the daily patterns of each binary data, by comparing the results of curve fitting each binary data. Based on the quantified information, it has the effect of being usable to correct the subject's daily rhythm.

[0032] Meanwhile, the effects of the present invention are not limited to those mentioned above, and other unmentioned technical effects will be clearly understood by a person skilled in the art from the following description.

[0033] FIG. 1 is a block diagram illustrating the configuration of a device for generating a sleep-wake cycle model using unconstrained sleep data according to an embodiment of the present invention.

[0034] FIG. 2 is a diagram illustrating an example of sensor data collected by a device according to an embodiment of the present invention.

[0035] FIG. 3 is a diagram showing the results of classifying sleep stages using heart rate, respiratory rate, and movement information derived from a device according to one embodiment of the present invention.

[0036] FIG. 4 is a diagram showing an example of binarized bed occupancy data in a device according to an embodiment of the present invention.

[0037] FIG. 5 is a diagram showing an example of binarized heart rate data in a device according to an embodiment of the present invention.

[0038] FIG. 6 is a diagram showing an example of sunrise-sunset binary data generated based on sunrise-sunset times in a device according to an embodiment of the present invention.

[0039] FIG. 7 is a diagram showing a circadian rhythm estimated by a device according to one embodiment of the present invention.

[0040] FIG. 8 is a diagram illustrating an example of binary data accumulated over m days in a device according to an embodiment of the present invention.

[0041] FIG. 9 is a graph showing the sleep homeostasis estimated in a device according to one embodiment of the present invention.

[0042] FIG. 10 is a diagram showing a sleep-wake cycle model estimated in a device according to one embodiment of the present invention.

[0043] FIG. 11 is a graph showing circadian rhythm and sleep homeostasis according to changes in weights output from a device according to one embodiment of the present invention.

[0044] FIG. 12 is a flowchart illustrating the operation of generating a sleep-wake cycle model using unconstrained sleep data in a device according to one embodiment of the present invention.

[0045] FIG. 13 is a diagram illustrating an operation in which a subject is induced to have a high level of alertness after waking up in a device according to an embodiment of the present invention.

[0046] FIG. 14 is a diagram illustrating the operation of improving a subject's Monday blues in a device according to an embodiment of the present invention.

[0047] FIG. 15 is a diagram illustrating an operation to improve the sleep inertia of a subject in a device according to an embodiment of the present invention.

[0048] FIG. 16 is a diagram illustrating the operation of improving jet lag in a subject in a device according to an embodiment of the present invention.

[0049] FIG. 17 is a diagram illustrating the operation of improving jet lag in a subject in a device according to an embodiment of the present invention.

[0050] FIG. 18 illustrates an example of providing information regarding fatigue to a subject by utilizing the degree of mismatch in the circadian rhythm graph.

[0051] Figure 19 is a diagram illustrating another way of representing a sleep homeostasis graph.

[0052] Figure 20 illustrates a device providing daily activity guide information or sleep guide information to a subject.

[0053]

[0054] Detailed information regarding the purpose, technical configuration, and resulting effects of the present invention will be more clearly understood through the following detailed description based on the drawings attached to the specification of the present invention. An embodiment according to the present invention will be described in detail with reference to the attached drawings.

[0055] The embodiments disclosed herein should not be interpreted or used to limit the scope of the invention. To a person skilled in the art, the description including the embodiments herein has various applications. Accordingly, any embodiments described in the detailed description of the invention are illustrative to better explain the invention and are not intended to limit the scope of the invention to the embodiments.

[0056] The functional blocks shown in the drawings and described below are merely examples of possible implementations. In other implementations, other functional blocks may be used without departing from the spirit and scope of the detailed description. Additionally, while one or more functional blocks of the present invention are shown as individual blocks, one or more of the functional blocks of the present invention may be a combination of various hardware and software configurations that perform the same function.

[0057] Furthermore, the expression "including certain components" is an "open-ended" expression that merely designates the existence of said components and should not be understood as excluding additional components. Moreover, when it is stated that a component is "connected" or "joined" to another component, it should be understood that while the component may be directly connected or joined to that other component, there may also be other components present in between.

[0058] FIG. 1 is a block diagram illustrating the configuration of a device for generating a sleep-wake cycle model using unconstrained sleep data according to an embodiment of the present invention.

[0059] Referring to FIG. 1, the device (100) may include a processor (110), a sensor module (120), a display (130), and a memory (140).

[0060] According to one embodiment of the present invention, a device (100) can be attached to a bed to measure sleep data of a subject lying on the bed. The sleep data may include sleep time, pressure, and vibration data. In this case, since the device (100) measures sleep data while the subject is lying on the bed without any conditions, the subject does not need to follow any sleep conditions or wear a sleep device, allowing for the unconditional measurement of sleep data in a normal sleep environment.

[0061] According to one embodiment of the present invention, the processor (110) can measure sleep data of a subject, generate a sleep-wake cycle model using the measured sleep data, and perform the overall operation of the device (100) for outputting the generated sleep-wake cycle model.

[0062] According to one embodiment of the present invention, the sensor module (120) may include a pressure sensor and a vibration sensor for measuring the heart rate or movement of a subject, and may also include various sensors for detecting various movements such as the subject's heart rate. Additionally, the sensor module (120) may be installed in multiple locations on the bed to detect the subject's sleep data at various locations.

[0063] According to one embodiment of the present invention, the display (130) can output a plurality of binary data estimated by the processor (110) and a sleep-wake cycle model generated based on said binary data. Additionally, the display (130) includes a touchscreen, and when displaying a survey screen through said touchscreen, the touch input regarding the degree of fatigue or alertness of said subject entered through said survey screen can be verified as a survey.

[0064] According to one embodiment of the present invention, the memory (140) can store a plurality of previously generated sleep-wake cycle models.

[0065] According to one embodiment of the present invention, a processor (110) can detect vibration data and pressure data while a subject is sleeping on the bed using a sensor module (120) attached to the bed, and can estimate the subject's sleep time, circadian rhythm, and sleep homeostasis for a certain period using the detected vibration data and pressure data.

[0066] According to one embodiment of the present invention, the processor (110) determines the vibration data as the subject's heart rate data, binarizes the time intervals before and after the time when the lowest heart rate value appears in the heart rate data and the remaining time intervals, and determines the data including the binarized time intervals as a circadian rhythm for autonomic nervous system activity. Subsequently, the processor (110) determines a weight set for the subject and applies the weight to the circadian rhythm to estimate the circadian rhythm according to the weight.

[0067] According to one embodiment of the present invention, a processor (110) can obtain sunrise and sunset times from a server providing time (e.g., Google, Naver). At this time, the processor (110) sets the time from sunrise to sunset to '1', binarizes the remaining times to '0', and can estimate a circadian rhythm using the binarized sunrise-sunset binary data (Cs(t)). Accordingly, the processor (110) can estimate a circadian rhythm by considering the sunrise and sunset times, taking into account the characteristics of the circadian rhythm that change according to sunlight.

[0068] According to one embodiment of the present invention, a processor (110) can estimate a circadian rhythm by additionally considering bed occupancy data (Cp) and heart rate binary data (Ch) using the lowest heart rate in addition to sunrise-sunset binary data (Cs(t)) to estimate the circadian rhythm in situations that change the circadian rhythm other than sunlight (e.g., shift work, business trip, etc.). Subsequently, the processor (110) can derive Ct by weighting Cs, Cp, and Ch, and derive a circadian rhythm (C) by curve fitting the derived Ct. According to one embodiment of the present invention, the processor (110) can binarize the pressure data to identify it as bed occupancy data indicating whether the subject occupies the bed.

[0069] According to one embodiment of the present invention, the processor (110) can generate a sleep-wake cycle model by accumulating the estimated circadian rhythm and sleep homeostasis over the specified period. For example, the processor (110) can output a sleep homeostasis graph representing the value of the sleep homeostasis over time using the bed occupancy data.

[0070] According to one embodiment of the present invention, the processor (110) can increase the value of sleep homeostasis in the section where the bed occupancy data is set to '1' in the sleep homeostasis graph to a specified value, and decrease the value of sleep homeostasis in the section where the bed occupancy data is set to '0' to a specified value.

[0071] According to one embodiment of the present invention, the processor (110) can identify the sleep time by dividing it into weekend sleep time and weekday sleep time, and calculate the standard required sleep time for the subject by weighting the identified weekend sleep time and weekday sleep time.

[0072] According to one embodiment of the present invention, if the detected sleep time corresponds to the reference required sleep time, the processor (110) can set the value of the sleep homeostasis of the sleep homeostasis graph to '0'.

[0073] According to one embodiment of the present invention, if the processor (110) determines, based on the vibration data, that the subject wakes up more than a specified number of times during sleep, the decreasing slope of the sleep homeostasis graph can be set to less than or equal to a set angle value.

[0074] According to one embodiment of the present invention, the processor (110) can identify a first time interval during the specified period in which the value of the circadian rhythm is greater than the value of sleep homeostasis, and a second time interval during the specified period in which the value of the circadian rhythm is smaller than the value of sleep homeostasis. For example, the processor (110) can determine that the level of arousal of the subject in the first time interval is higher than the arousal reference value, and determine that the level of arousal of the subject in the second time interval is lower than the arousal reference value.

[0075] According to one embodiment of the present invention, a processor (110) can generate a sleep-wake cycle model with weights applied to the subject and control a display (130) so that the generated sleep-wake cycle model is output. Additionally, the processor (110) can output the sleep-wake cycle model including a comparison result with the wakefulness reference value for the first and second time intervals.

[0076] A device (100) according to one embodiment of the present invention may further include a vibration device that outputs vibration data. For example, a processor (110) may control the vibration device to output vibration data in order to induce a subject's heart rate and regulate autonomic nervous system activity.

[0077] According to one embodiment of the present invention, the device (100) can be used to output vibration data to help a subject recover during sleep and to improve disorders related to circadian rhythms (e.g., sleep inertia, Monday blues, jet lag). Additionally, the device (100) can output vibration data to induce a slow heart rate during sleep, thereby increasing the activity of the parasympathetic nervous system and causing the slope of the sleep homeostasis graph to decrease steeply.

[0078] According to one embodiment of the present invention, the device (100) can increase sympathetic nervous system activity by outputting preset vibration data k minutes before waking up to induce the subject's heart rate to increase. Accordingly, the circadian rhythm in the circadian rhythm graph can be advanced.

[0079] According to one embodiment of the present invention, by generating a sleep-wake cycle model and estimating the degree of fatigue and arousal during daytime activities, a sleep-wake cycle model is generated that causes a subject to increase daytime arousal and decrease fatigue, and a method and timing of stimulation during sleep to follow the generated model are determined, and the method can be provided to the subject at the determined time during sleep.

[0080] FIG. 2 is a diagram illustrating an example of sensor data collected by a device according to an embodiment of the present invention.

[0081] According to one embodiment of the present invention, a device (100) installed on a bed can detect vibration data and pressure data through a sensor module (120).

[0082] Referring to FIG. 2, the device (100) can output the value of pressure data for each sleep period as a pressure sensor value based on the detected pressure data. In addition, the device (100) can check the value of vibration data for each sleep period and output the value of vibration data as an average heart rate.

[0083] FIG. 3 is a diagram showing the results of classifying sleep stages using heart rate, respiratory rate, and movement information derived from a device according to one embodiment of the present invention.

[0084] According to one embodiment of the present invention, a device (100) installed on a bed detects vibration data and pressure data through a sensor module (120), and can determine heart rate, respiratory rate, and movement information during the subject's sleep period using the detected vibration data and pressure data.

[0085] Referring to FIG. 3, the device (100) sums up heart rate, respiratory rate, and movement information determined for each sleep period, and can determine the sleep stage as REM sleep (RS), wake (W), light sleep (LS), or deep sleep (DS) according to the value of the summed data.

[0086]

[0087] FIG. 4 is a diagram showing an example of binarized bed occupancy data in a device according to an embodiment of the present invention.

[0088] Referring to FIG. 4, when the device (100) derives a pressure sensor value from pressure data, it can binarize the pressure sensor value for a certain sleep period (e.g., 23:00 to 9:00) into bed occupancy data representing the time spent occupying the bed. For example, as the binarization is performed, the bed occupancy data may be output to have a value of '1' or '0' for the corresponding sleep period.

[0089] According to one embodiment of the present invention, the device (100) may set the interval of the subject's bed occupancy data that is not '0' to '1'. For example, the device (100) may set the interval of the bed occupancy data that is not '0' to '1' to consider the effect of the subject's lifestyle rhythm, such as rest or activity, on the circadian rhythm. As the interval of the bed occupancy data is set to '0', the circadian rhythm may be output based on irregularities in lifestyle rhythm, such as shift work, and activities such as weekend naps.

[0090] FIG. 5 is a diagram showing an example of binarized heart rate data in a device according to an embodiment of the present invention.

[0091] Referring to FIG. 5, the device (100) can derive a heart rate for a certain sleep period (e.g., 23:00 to 9:00) from vibration data and binarize the derived heart rate to output it as heart rate binary data.

[0092] According to one embodiment of the present invention, in the process of performing the binarization, the device (100) may determine a specific point in time during the corresponding sleep period, for example, the point in time when the lowest heart rate is detected by the user, and may set an arbitrary time period before and after this point in time to 0 and the remaining time period to 1. Specifically, the device (100) curve-fits heart rate data collected during the sleep period, sets the interval from the time before a preset value (-a hours) to the time after a preset value (a hours) based on the time when the lowest heart rate appears to be '0', and sets a value of '1' for the remaining interval to obtain heart rate binary data (C h (t)) can be output.

[0093] FIG. 6 is a diagram showing an example of sunrise-sunset binary data generated based on sunrise-sunset times in a device according to an embodiment of the present invention.

[0094] Referring to FIG. 6, the device (100) can generate sunrise-sunset binary data (Cs(t)) based on sunrise and sunset time information of the corresponding day, binarizing the time from sunrise to sunset into '1' and the remaining time into '0'. For example, to reflect the influence of light among external stimuli affecting the circadian rhythm, the device (100) can generate sunrise-sunset binary data.

[0095] FIG. 7 is a diagram showing a circadian rhythm estimated by a device according to one embodiment of the present invention.

[0096] Referring to FIG. 7, the device (100) obtains circadian rhythm data (C) by weighted summing sunrise-sunset binary data based on sunrise-sunset time information, bed occupancy data, and heart rate binary data. t (t)) can be verified. At this time, the device (100) can perform the weighted sum by multiplying each data by a weight (e.g., 1 to 3 times) according to the importance of each of the sunrise-sunset binary data, bed occupancy data, or heart rate binary data. Although various factors may affect a person's circadian rhythm, the present invention may be characterized by calculating circadian rhythm data such as FIG. 7 based on the premise that data obtained from the user based on sunrise-sunset, bed occupancy, and heart rate has a significant influence.

[0097] Mathematical formula 1 is a formula for calculating circadian rhythm data according to one embodiment of the present invention.

[0098] [Mathematical Formula 1]

[0099]

[0100] Here, C t (t) is a circadian rhythm for a specific time (t), and C s (t) is sunrise-sunset binary data based on sunrise-sunset time information, and C p(t) is bed occupancy data binarized over 24 hours based on pressure data, and C h (t) is data obtained by binarizing the time from the time of the onset of the lowest heart rate in a 24-hour perspective, setting the time from the time a before to the time a after as '0' and the remaining times as '1'. Additionally, C(t) at the bottom of the figure is C t This shows the graph after curve fitting (t) with a cosine waveform.

[0101] According to one embodiment of the present invention, the device (100) may receive information regarding the subject's daytime or nighttime characteristics from the subject. Subsequently, the device (100) may divide the received information to determine the basic cycle of the circadian rhythm. For example, if the information is divided into three parts, the device (100) may determine the basic cycle of the intermediate type to be 24 hours, the basic cycle of the daytime type to be shorter than 24 hours, and the basic cycle of the nighttime type to be longer than 24 hours.

[0102] For reference, the preceding description partially mentioned the process of determining the point in time when the user's lowest heart rate is detected during sleep. This may include a process in which the device (100) fits the heart rate measured during sleep into an n-th degree polynomial, determines the time when the polynomial has the lowest value on the graph as the point in time when the lowest heart rate is detected, and determines the heart rate detected at that time as the lowest heart rate. Subsequently, the device (100) considers the influence of light among external stimuli affecting the circadian rhythm, sunrise-sunset binary data (C s (t)), binarized heart rate data based on the lowest heart rate considering the subject's autonomic nervous system activity (C h (t)), and bed occupancy data considering the user's lifestyle habits (C p The circadian rhythm can be calculated by considering (t).

[0103] According to one embodiment of the present invention, the device (100) unconditionally measures the data that may affect the circadian rhythm during the subject's sleep, and calculates the circadian rhythm (C) by calculating a weighted sum of the data using weights (e.g., x1, x2, x3). t (t)) can be calculated. In addition, the device (100) can set the weights by reflecting the importance of each data affecting the circadian rhythm and the individual sensitivity that differs for each subject to the circadian rhythm. For example, if the device (100) has a large influence of light on the circadian rhythm, sunrise-sunset binary data (C s The weight (x1) of (t) can be set to a higher value than the other weights (x2, x3).

[0104] According to one embodiment of the present invention, the device (100) has the calculated circadian rhythm (C t The estimated circadian rhythm (C(t)) can be determined by fitting (t)) to a curve.

[0105] FIG. 8 is a diagram illustrating an example of binary data accumulated over m days in a device according to an embodiment of the present invention.

[0106] Referring to FIG. 8, the device (100) can accumulate binary data from m days prior to a reference date, taking into account the inertia of the circadian rhythm influenced by past rhythms. Subsequently, the device (100) can estimate the circadian rhythm by applying a higher weight value to data for days closer to the reference date.

[0107] FIG. 9 is a graph showing the sleep homeostasis estimated in a device according to one embodiment of the present invention.

[0108] According to one embodiment of the present invention, the device (100) can estimate sleep homeostasis using bed occupancy data based on pressure data and output the estimated sleep homeostasis as a sleep homeostasis graph. For example, the device (100) can estimate sleep homeostasis by increasing the homeostasis (S(t)) during the time set to '1' in the bed occupancy data and decreasing the homeostasis (S(t)) during the time set to '0'. Additionally, if the device (100) determines that the subject has slept for a standard required sleep time, it can set the homeostasis (S(t)) at the time the required sleep time has elapsed to a value of '0'.

[0109] Referring to FIG. 9, the device (100) can set the slope of the decrease of the sleep homeostasis graph based on the number of times the subject wakes up during sleep and the heart rate variability index during sleep. For example, if it is determined that the subject wakes up more than a specified number of times during sleep, the device (100) can set the slope of the decrease to an angle less than or equal to a set angle (901) so that the slope of the decrease becomes gentler.

[0110] In the bottom graph of FIG. 9, if it is determined that the subject is engaging in non-sleep activities, such as sitting on the bed to read a book or looking at a smartphone, based on the bed occupancy data, the device (100) can estimate sleep homeostasis more accurately by reducing the slope (904) in that section. By analyzing the collected pressure data, the posture of the subject, such as whether they are lying or sitting on the bed, can be estimated, and the results of this estimation can be utilized to identify the subject's non-sleep activities. Additionally, the subject's activity can be estimated by referencing vibration data, that is, the subject's heart rate, in addition to pressure data. If the subject continues to be active after waking up, the slope of the sleep homeostasis graph will rise at a specific slope; however, if the subject takes a rest in between, the slope will be displayed relatively gently during that time, thereby allowing the rest taken by the subject to be reflected in the sleep homeostasis estimation.

[0111] Meanwhile, when sleep homeostasis is cumulatively estimated by the device, unlike a circadian rhythm that increases and decreases with a period within a certain range, sleep homeostasis can theoretically increase or decrease from minus infinity to plus infinity. In estimating sleep homeostasis having this characteristic, it is necessary to correct the initial value as needed. The device (100) according to the present invention can provide a user interface to receive feedback from a subject, i.e., a user. For example, the device can have the subject input a level of fatigue from 1 to 10 or ask questions to gauge the subject's sleep needs, and receive feedback on this so that the initial value or reference value required to estimate sleep homeostasis can be corrected.

[0112] According to one embodiment of the present invention, the device (100) can evaluate the degree of autonomic nervous system activity using the heart rate variability index, which quantifies the degree of change in heart rate interval. For example, autonomic nervous system activity is divided into sympathetic and parasympathetic activities, and parasympathetic nervous system activity may increase during sleep. Accordingly, the device (100) checks the heart rate variability index that evaluates parasympathetic nervous system activity, and if the value of the checked index is greater than or equal to a threshold value, the slope of the decrease of the homeostasis graph can be set greater than the set angle.

[0113] According to one embodiment of the present invention, the subject often cannot get as much sleep as they want on weekdays due to going to work or school, and may tend to sleep more on weekends to compensate for this. At this time, the device (100) calculates the subject's required sleep time using the weighted average of weekend sleep time and weekday sleep time, and can confirm the calculated required sleep time as the standard required sleep time.

[0114] According to one embodiment of the present invention, the device (100) can reduce (902) the value of the sleep homeostasis graph up to the time of waking up if the subject's sleep time is less than the standard required sleep time. Additionally, if the device (100) determines that the parasympathetic nervous system activity is high during sleep for the subject, it can adjust (903) the slope of the graph to a specified value.

[0115] FIG. 10 is a diagram showing a sleep-wake cycle model estimated in a device according to one embodiment of the present invention.

[0116] Referring to FIG. 10, the device (100) can generate a sleep-wake cycle model (1000) by accumulating a sleep homeostasis graph and a circadian rhythm graph.

[0117] According to one embodiment of the present invention, the device (100) can determine that for a subject, the degree of arousal is high at times when the circadian rhythm graph (1001) is above the sleep homeostasis graph (1002), and the degree of arousal is low at times when the sleep homeostasis graph (1002) is above the circadian rhythm graph (1001). At this time, the device (100) can output the sleep-wake cycle model (1000) by marking the time when the degree of arousal is expected to be low in gray.

[0118] FIG. 11 is a graph showing circadian rhythm and sleep homeostasis according to changes in weights output from a device according to one embodiment of the present invention.

[0119] Referring to FIG. 11, the device (100) may receive a survey from a subject regarding the degree of fatigue or alertness at specific time intervals during initial operation. Subsequently, the device (100) may select a model (1100) corresponding to the input survey from among pre-generated sleep-wake cycle models.

[0120] According to one embodiment of the present invention, the device (100) evaluates when the subject's level of arousal is high or low during the day based on the pattern of the circadian rhythm (1101) and sleep homeostasis graph (1102) included in the selected model, and can design a daytime activity plan for the subject based on the evaluation.

[0121] According to one embodiment of the present invention, the device (100) can set the value of the circadian rhythm (1101) with weights of binary data, and set the slope of the decrease of the sleep homeostasis graph (1102) during sleep with weights of the number of times waking up during sleep and parasympathetic nervous system activity.

[0122] According to one embodiment of the present invention, the device (100) can generate various sleep-wake cycle models by adjusting the weights of the estimated binary data.

[0123] According to one embodiment of the present invention, the device (100) can generate a plurality of sleep-wake cycle models by applying weights to a circadian rhythm (1101) and a sleep homeostasis graph (1102) for a preset period of m days during initial operation. Subsequently, the device (100) evaluates the degree of arousal of the input subject and can finally determine the model among the generated plurality of sleep-wake cycle models that has a weight with a high degree of agreement with the evaluated degree of arousal as the sleep-wake cycle model for the subject.

[0124] FIG. 12 is a flowchart illustrating the operation of generating a sleep-wake cycle model using unconstrained sleep data in a device according to one embodiment of the present invention.

[0125] Referring to FIG. 12, in operation S110, the device (100) can detect vibration data and pressure data while the subject is sleeping on the bed by using a sensor module (120) attached to the bed.

[0126] According to one embodiment of the present invention, the device (100) can determine the vibration data as the heart rate data of the subject. For example, the device (100) binarizes a certain time interval before and after the time when the lowest heart rate value appears in the heart rate data and the remaining time interval, and can determine the binary heart rate data including the binarized time intervals as a circadian rhythm for autonomic nervous system activity.

[0127] According to one embodiment of the present invention, the device (100) can binarize the pressure data to confirm it as bed occupancy data indicating whether the subject occupies the bed.

[0128] In operation S120, the device (100) can estimate the sleep time, circadian rhythm, and sleep homeostasis of the subject over a certain period using the detected vibration data pressure data.

[0129] Additionally, at this stage, the device (100) can verify sunrise-sunset binary data (Cs(t)) in which the time from sunrise to sunset is set to '1' and the remaining time is binarized to '0', based on the sunrise and sunset times obtained from the server providing the time. Subsequently, the device (100) can verify the circadian rhythm by performing a weighted sum of the above sunrise-sunset binary data, bed occupancy data, and heart rate binary data.

[0130] According to one embodiment of the present invention, the device (100) can determine a weight set for the subject. For example, the device (100) can apply the weight to the circadian rhythm to estimate the circadian rhythm according to the weight.

[0131] According to one embodiment of the present invention, the device (100) can output a sleep homeostasis graph representing the value of sleep homeostasis over time using the bed occupancy data. Additionally, the device (100) can identify the sleep time by classifying it into weekend sleep time and weekday sleep time, and calculate the standard required sleep time for the subject by weighting the identified weekend sleep time and weekday sleep time.

[0132] According to one embodiment of the present invention, the device (100) can set the value of the sleep homeostasis of the sleep homeostasis graph to '0' when the detected sleep time corresponds to the reference required sleep time.

[0133] According to one embodiment of the present invention, the device (100) can increase the value of sleep homeostasis in the section where the bed occupancy data is set to '1' in the sleep homeostasis graph to a specified value, and decrease the value of sleep homeostasis in the section where the bed occupancy data is set to '0' in the sleep homeostasis graph to a specified value.

[0134] According to one embodiment of the present invention, if the device (100) determines, based on the vibration data, that the subject wakes up more than a specified number of times during sleep, the decreasing slope of the sleep homeostasis graph can be set to less than or equal to a set angle value.

[0135] In operation S130, the device (100) can generate a sleep-wake cycle model by accumulating the estimated circadian rhythm sleep homeostasis over the specified period.

[0136] In operation S140, the device (100) can output the generated sleep-wake cycle model to which weights set for the subject are applied.

[0137] According to one embodiment of the present invention, the device (100) can identify a first time interval during the specified period in which the value of the circadian rhythm is greater than the value of sleep homeostasis, and identify a second time interval during the specified period in which the value of the circadian rhythm is smaller than the value of sleep homeostasis. For example, the device (100) can determine, in comparison with the wakefulness reference value, that the subject's level of wakefulness is high in the first time interval and low in the second time interval.

[0138] According to one embodiment of the present invention, the device (100) can output the result of comparison with the wakefulness reference value for the first and second time intervals to the sleep-wake cycle model.

[0139] FIG. 13 is a diagram illustrating an operation in which a subject is induced to have a high level of alertness after waking up in a device according to an embodiment of the present invention.

[0140] According to one embodiment of the present invention, the device (100) can output vibration data that causes the subject to have a high level of alertness after waking up.

[0141] Referring to FIG. 13, the device (100) can control a vibration device to output vibration data that induces a slow heart rate of the subject during sleep, thereby increasing the parasympathetic nervous system activity of the subject. Additionally, the device (100) can output vibration data that increases the degree of recovery of the subject during sleep or adjusts the slope of the sleep homeostasis graph (dotted line) so that the value decreases steeply. Accordingly, in the sleep homeostasis graph of the subject after waking up output from the device (100), the time spent above the circadian rhythm graph (solid line) (time during which the level of arousal remains low) can be reduced.

[0142] According to one embodiment of the present invention, the device (100) can increase the sympathetic nervous system activity of the subject by outputting vibration data that rapidly induces a heart rate k minutes before waking up. Accordingly, the subject's circadian rhythm is advanced, and the time during which the subject's sleep homeostasis graph is above the circadian rhythm graph (the time during which the level of arousal remains low) after waking up can be reduced.

[0143] According to one embodiment of the present invention, the device (100) can maximize the effect of minimizing the time during which the subject's level of arousal remains low during daytime activities after waking up by performing an action that increases the activity of the parasympathetic or sympathetic nervous system. Additionally, the device (100) can output vibration data by adjusting the timing and duration of heart rate reduction and increase stimuli. At this time, the device (100) can output vibration data by evaluating the time during daytime activities when the circadian rhythm lies above the sleep homeostasis graph, and adjusting the timing and duration of the stimulation in a direction that maximizes this time.

[0144] FIG. 14 is a diagram illustrating the operation of improving a subject's Monday blues in a device according to an embodiment of the present invention.

[0145] According to one embodiment of the present invention, the device (100) can determine that the subject feels great fatigue on Monday as the regularity of the subject's circadian rhythm, generated by living a regular life for going to work on weekdays, is disrupted by sleep-daily patterns such as going to bed late and waking up late on weekends.

[0146] Referring to FIG. 14, the device (100) can estimate a circadian rhythm (solid line) using weekday data and estimate a circadian rhythm (dotted line) including weekend data to calculate the delay time between the two rhythms.

[0147] According to one embodiment of the present invention, the device (100) can increase the sympathetic nervous activity of a subject by outputting vibration data that rapidly induces the subject's heart rate k minutes before waking up from sleep on the day transitioning from a weekend to a weekday. Accordingly, by advancing the weekend circadian rhythm (dotted line), it can support the subject approaching the weekday circadian rhythm (solid line). Additionally, the device (100) can determine the time point of k minutes based on the degree of phase delay at this time.

[0148] While previously known methods for alleviating Monday blues required interventions such as individuals adjusting their lifestyle patterns by maintaining a regular routine even on weekends, the present invention estimates the circadian rhythm by including additional weekend data and calculates the difference between weekdays and weekends, thereby outputting corresponding vibration data on the day transitioning from the weekend to the weekday. Accordingly, the present invention can increase the subject's sympathetic nervous system activity to advance the weekend circadian rhythm to the weekday circadian rhythm, thereby preventing the subject from feeling fatigued on weekdays.

[0149] FIG. 15 is a diagram illustrating an operation to improve the sleep inertia of a subject in a device according to an embodiment of the present invention.

[0150] Referring to FIG. 15, the device (100) can output vibration data that causes an increase in the subject's heart rate at the alarm setting time when the subject's circadian rhythm graph (black) increases after the lowest point and approaches the alarm setting time.

[0151] According to one embodiment of the present invention, the device (100) can output vibration data so that heart rate stimulation occurs from a relatively early time to sufficiently increase sympathetic nervous system activity when the lowest point in the subject's circadian rhythm graph (gray) approaches the alarm setting time.

[0152] According to one embodiment of the present invention, the device (100) can set the output time of vibration data for heart rate increase according to the time of the lowest point in the subject's circadian rhythm.

[0153] FIGS. 16 and FIGS. 17 are drawings illustrating an operation to improve jet lag in a subject in a device according to an embodiment of the present invention.

[0154] Referring to FIG. 16, as the subject moves from east to west, the device (100) can output vibration data that causes the subject's circadian rhythm to be delayed.

[0155] According to one embodiment of the present invention, as the subject moves from east to west, the device (100) can delay the circadian rhythm so that the subject goes to sleep late and wakes up late. For example, the device (100) can delay the circadian rhythm by delaying the subject's parasympathetic activity by outputting vibration data that prevents the subject's heart rate from slowing down during sleep.

[0156] According to one embodiment of the present invention, the device (100) can set a time at which the output of vibration data is terminated according to the degree of delay of the circadian rhythm, and control the output of vibration data until the set time.

[0157] Referring to FIG. 17, as the subject moves from west to east, the device (100) can output vibration data that causes the subject's circadian rhythm to advance.

[0158] According to one embodiment of the present invention, as the subject moves from west to east, the device (100) can advance the circadian rhythm so that the subject goes to sleep early and wakes up early. For example, the device (100) can advance the circadian rhythm by increasing the subject's sympathetic nervous activity by outputting vibration data that causes the heart rate to increase at the time the subject wakes up.

[0159] According to one embodiment of the present invention, the device (100) can set a time at which the output of vibration data begins according to the degree to which the circadian rhythm is advanced, and control the output of vibration data from the set time.

[0160] According to one embodiment of the present invention, the device (100) outputs vibration data that causes the circadian rhythm to be delayed or advanced, thereby improving the jet lag of a subject or adjusting the circadian rhythm of a shift worker with irregular commuting times.

[0161] FIG. 18 illustrates another embodiment of the present invention, which provides information to a subject, i.e., a user, by estimating how much fatigue there will be on the day by utilizing the degree of mismatch in the circadian rhythm graph. The above information regarding fatigue may be understood as one of the sleep guide information or activity guide information generated by the device (100) for the subject using a sleep-wake cycle model.

[0162] When referring to the drawing, the solid line (1801) represents the subject's first circadian rhythm estimated based on data from the past n days excluding today, i.e., the circadian rhythm reflecting the user's usual sleep pattern, and the dotted line (1802) represents the second circadian rhythm estimated based on data collected up to the time the subject woke up today, i.e., the circadian rhythm reflecting the user's sleep state on that day. In many cases, since a person's sleep pattern is not constant every day, deviations may occur between the first circadian rhythm and the second circadian rhythm. When both circadian rhythm graphs are fitted in the form of Acos(2πf+φ)+B and compared, differences in amplitude, mesor, and acrophase can be calculated as shown in the drawing. For reference, in the circadian rhythm graph, amplitude represents the intensity of the sleep-wake cycle; the larger the amplitude, the more distinct the transition between sleep and wakefulness, and conversely, the smaller the amplitude, the more ambiguous the boundary between sleep and wakefulness. Furthermore, the median represents the average level of the sleep-wake cycle—in other words, the level of alertness—and a higher median indicates a higher level of alertness. The peak phase indicates the point in time when alertness is highest, and it can serve as an indicator to monitor whether the subject's sleep time is delayed or accelerated. Referring again to the diagram, the deviation calculated from the two circadian rhythm graphs allows one to gauge the degree of discrepancy between the two rhythms, and this discrepancy can be utilized to predict the subject's fatigue level for the day. For example, if the peak phase of the second circadian rhythm is delayed and its amplitude is smaller compared to the first circadian rhythm, it is possible that the subject fell asleep late the previous day or did not sleep comfortably; this can serve as grounds to predict high fatigue levels for the day.The device (100) according to the present invention may be implemented to provide information on the day's fatigue level to a subject based on the degree of inconsistency of the circadian rhythm graph in this manner, and in this process, the first circadian rhythm and the second circadian rhythm graph may be implemented to be output together.

[0163] FIG. 19 is a diagram illustrating another method of representing a sleep homeostasis graph. FIG. 9, mentioned earlier, illustrates a sleep homeostasis graph estimated by the device (100) according to an embodiment of the present invention. It can be seen that the sleep homeostasis graph in FIG. 9 is represented in the form of a straight line with a slope that bends. However, considering the meaning of sleep homeostasis, since a person's sleep desire generally does not increase indefinitely beyond a certain level and does not decrease indefinitely below a certain level, it is necessary to represent the graph by taking into account the characteristics of sleep homeostasis. That is, no matter how much one stays awake all night, the sleep desire will rise to a certain level and reach saturation, and that state will be maintained, so the sleep desire will no longer increase or the rate of increase will become very slow.

[0164] FIG. 19 attempts to represent a graph in a new form by taking into account the characteristics of such sleep homeostasis, and the sleep homeostasis graph can have a shape as shown in the figure. That is, when an upper or lower limit is determined within a specific interval, the sleep homeostasis graph can have a curved graph shape that starts with a relatively large value of the tangent slope at the beginning and gradually takes on a smaller value of the tangent slope. The figure shows that the sleep homeostasis graph can have a curve in each interval when proceeding from the first upper limit (1901) to the first lower limit (1902), and then from the first lower limit (1902) to the next second upper limit (1903). Meanwhile, it is understood that the detailed description and figure do not limit the shape of the graph for more accurately representing sleep homeostasis to any specific function, and any function that can be expressed in the form of a curve as it proceeds to a vertex in each interval, such as a logarithmic function, as shown in the figure, can be utilized. According to a preferred embodiment, various types of functions utilizing exponential decay functions (y = 1 - a * exp(-x / b)) can be used to represent a water surface homeostasis graph.

[0165] For reference, regarding the method of determining the upper limit of the sleep sidereality graph, that is, the value corresponding to the sleep desire at saturation, i) the saturation state can be defined as a point that is greater than or equal to n times (e.g., 1.5 times, 2 times, 3 times, etc.) the magnitude of the peak of the circadian rhythm, and the graph can be drawn by determining that point as the upper limit, or ii) under the assumption that the subject cannot stay awake for more than n hours (e.g., 36 hours, etc.) without dozing off for more than a certain amount of time, the sleep desire can be defined as having reached saturation n hours after the subject wakes up, and the graph can be drawn by determining that point as the upper limit. On the other hand, when determining the lower limit, the lowest point of the circadian rhythm can be determined as the lower limit, and the curve graph can be drawn accordingly.

[0166] FIG. 20 illustrates a device (100) according to the present invention providing daily activity and sleep guides to a subject. More precisely, it illustrates a device that derives a circadian rhythm and sleep homeostasis graph according to a sleep-wake cycle model and provides a guide on when the subject should perform main activities, when to sleep, and when to wake up based on the above.

[0167] When briefly examining the process of providing guidance to the subject, the device (100) first generates a sleep-wake cycle model based on data collected and analyzed up to the time of waking up, and can derive a circadian rhythm and sleep homeostasis graph (2002) predicted after the time of waking up according to the generated model, and furthermore, can output this so that the subject can view it. Based on this graph, it can further display when the subject is expected to sleep (expected sleep) and when the subject is expected to wake up (expected wake-up). In addition, the device (100) can provide a sleep homeostasis graph (2001) suitable for the subject and simultaneously display when the subject should perform main activities (main activity suggestion; section A), when the subject should sleep (sleep suggestion), and when the subject should wake up (wake-up suggestion). Additionally, when referring to the drawing, the activity and sleep guide information provided to the subject may include a time period or interval (C) in which the subject's level of alertness is expected to be high when living according to their usual habits, and a time period or interval (B) in which the level of alertness is expected to be high when living according to the recommendations of the device (100). As can be seen in the drawing, it is expected that the time during which the level of alertness remains high after waking up will be longer when living according to the recommendations of the device (100).

[0168] The present invention has been examined above. Meanwhile, the present invention is not limited to the specific embodiments and applications described above, and it is understood that various modifications can be made by those skilled in the art without departing from the essence of the invention as claimed in the claims, and such modifications should not be understood as being distinct from the technical spirit or perspective of the present invention.

[0169] In particular, the configurations that implement the technical features of the present invention included in the block diagrams and flowcharts attached to this specification refer to the logical boundaries between said configurations. However, according to embodiments of software or hardware, the illustrated configurations and their functions are executed in the form of standalone software modules, monolithic software structures, code, services, and combinations thereof, and since the functions can be implemented by storing them on a medium executable on a computer equipped with a processor capable of executing stored program code, instructions, etc., all such embodiments should also be considered to fall within the scope of the present invention.

[0170] Accordingly, while the attached drawings and descriptions explain the technical features of the present invention, a specific arrangement of software for implementing these technical features should not be merely inferred unless explicitly stated. That is, various embodiments described above may exist, and since such embodiments may be modified in some way while retaining the same technical features as the present invention, they should also be considered to fall within the scope of the present invention.

[0171] In addition, while flowcharts depict operations in a specific order, they are illustrated to achieve the most desirable results and should not be understood as requiring that such operations be executed in the specific or sequential order depicted, or that all depicted operations must be executed. In certain cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of the various system components of the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together into a single software product or packaged into multiple software products.

Claims

1. A method for utilizing a sleep-wake cycle model generated based on unrestrained sleep data, wherein a device equipped with a processor, a sensor module, and a memory, An operation to detect vibration data and pressure data while the subject is sleeping in the bed using a sensor module; An operation to estimate the sleep duration, circadian rhythm, and sleep homeostasis of the above subject over a certain period; The operation of generating a sleep-wake cycle model by accumulating estimated circadian rhythms and sleep homeostasis over the above-mentioned period; and The action of providing sleep guide information or activity guide information generated using the above sleep-wake cycle model to the subject; including, How to utilize the sleep-wake cycle model.

2. In Paragraph 1, The operation of estimating the circadian rhythm of the above subject is, The operation of weighted summing sunrise-sunset binary data based on sunrise-sunset time information, bed occupancy data indicating whether the subject occupies the bed, and heart rate binary data based on the subject's heart rate data; and The operation of curve fitting the above-mentioned weighted sum data into a cosine waveform; comprising How to utilize the sleep-wake cycle model.

3. In Paragraph 1, The operation of estimating the sleep homeostasis of the above subject is, Using bed occupancy data indicating whether the subject occupies the bed, the method includes increasing sleep homeostasis during the time determined that the subject is not occupying the bed and decreasing sleep homeostasis during the time determined that the subject is occupying the bed. How to utilize the sleep-wake cycle model.

4. In Paragraph 3, The operation of estimating the sleep homeostasis of the above subject is, A method including the operation of setting the slope of a sleep homeostasis graph differently depending on the number of times the subject wakes up from sleep, the heart rate variability during sleep, or whether non-sleep activity is performed. How to utilize the sleep-wake cycle model.

5. In Paragraph 1, The act of providing sleep guide information or activity guide information generated using the above sleep-wake cycle model to the subject is, A method comprising providing information on the day's fatigue level to the subject by referring to the degree of discrepancy between the subject's first circadian rhythm estimated based on data collected over the past n days excluding the day, and the second circadian rhythm estimated based on data collected up to the time the subject woke up on the day. How to utilize the sleep-wake cycle model.

6. In Paragraph 1, The act of providing sleep guide information or activity guide information generated using the above sleep-wake cycle model to the subject is, An operation to output a graph of the circadian rhythm and sleep homeostasis predicted from the time of the subject's wake-up; and An action of displaying one of the predicted sleep time, indicating when the subject is expected to sleep based on the above graph, or the predicted wake time, indicating when the subject is expected to wake up; including, How to utilize the sleep-wake cycle model.

7. In Paragraph 6, The act of providing sleep guide information or activity guide information generated using the above sleep-wake cycle model to the subject is, The operation of outputting a sleep homeostasis graph suitable for the above subject; and An action indicating at least one of information regarding when the subject should perform the main activity, information regarding when the subject should sleep, information regarding when the subject should wake up, or a time period when the subject's level of arousal is expected to be high; including, How to utilize the sleep-wake cycle model.

8. In a device utilizing a sleep-wake cycle model generated based on unrestrained sleep data, A sensor module that detects vibration and pressure data while the subject is sleeping in bed; A device comprising: a processor that estimates the sleep time, circadian rhythm, and sleep homeostasis of the subject over a certain period using the detected vibration data and pressure data, generates a sleep-wake cycle model by accumulating the circadian rhythm and sleep homeostasis estimated over the certain period, and generates sleep guide information or activity guide information using the sleep-wake cycle model.

9. In Paragraph 8, A display that outputs the above sleep guide information or activity guide information; further comprising device.

Images