Systems and methods for increasing an individual's drowsiness

Abstract

A system includes a memory device that stores machine-readable instructions; and a control system including one or more processors configured to execute the machine-readable instructions to receive initial physiological data associated with a user, determine an initial drowsiness level of the user based at least in part on the initial physiological data, prompt the user via an electronic device to perform a first activity, receive subsequent physiological data associated with the user, determine a subsequent drowsiness level of the user based at least in part on the subsequent physiological data, and determine a first activity score indicative of the effectiveness of the first activity in altering the user's drowsiness based at least in part on the initial drowsiness level and the subsequent drowsiness level.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims the benefit and priority of U.S. Provisional Patent Application No. 62 / 985,777, filed on March 5, 2020. The entire disclosure of this document is incorporated herein by reference.

[0002] The present disclosure generally relates to systems and methods for monitoring insomnia and reducing insomnia - related symptoms, and more particularly to systems and methods for identifying or recommending one or more activities that increase a user's drowsiness and assist in promoting sleep.

Background Art

[0003] Many individuals suffer from insomnia (e.g., difficulty initiating sleep, frequent or long - duration awakenings after initial sleep onset, and early awakenings from which it is not possible to return to sleep) or other sleep - related disorders (e.g., periodic limb movement disorder (PLMD), obstructive sleep apnea (OSA), Cheyne - Stokes respiration (CSR), hypoventilation, obesity hypoventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD), etc.). Many of these sleep - related disorders can be treated or managed by prompting the user to perform one or more activities (e.g., watching media content) to assist in increasing the user's drowsiness level. One activity may be more effective than other activities in changing the user's drowsiness level. Therefore, there would be an advantage in determining or recommending personalized user - specific activities that increase the user's drowsiness and assist in managing insomnia symptoms. The present disclosure aims to solve these problems and others.

Summary of the Invention

[0004] According to some implementations of the present disclosure, a method includes receiving first physiological data associated with a user. The method also includes determining a first drowsiness level of the user based at least in part on the physiological data. The method also includes prompting the user to perform a first activity. The method also includes receiving subsequent physiological data associated with the user, where the subsequent physiological data is associated with the user while the user is performing the first activity, after the user has performed the activity, or both. The method also includes determining a subsequent drowsiness level of the user based at least in part on the subsequent physiological data. The method also includes determining a first activity score based at least in part on the first drowsiness level and the subsequent drowsiness level, where the first activity score indicates the effectiveness of the first activity in changing the user's drowsiness.

[0005] According to some implementations of the present disclosure, a system includes a memory storing machine-readable instructions and a control system including one or more processors configured to execute the machine-readable instructions to receive first physiological data associated with a user, where the first physiological data is generated by a sensor. The control system is further configured to determine a first drowsiness level of the user based at least in part on the first physiological data. The control system is further configured to prompt the user to perform a first activity via an electronic device. The control system is further configured to receive subsequent physiological data associated with the user, where the subsequent physiological data is generated by the sensor while the user is performing the first activity, after the user has performed the activity, or both. The control system is further configured to determine a subsequent drowsiness level of the user based at least in part on the subsequent physiological data. The control system is further configured to determine a first activity score based at least in part on the first drowsiness level and the subsequent drowsiness level, where the first activity score indicates the effectiveness of the first activity in changing the user's drowsiness.

[0006] According to some implementations of the present disclosure, a method includes receiving first physiological data associated with a user. The method also includes determining an initial drowsiness level of the user based at least in part on the first physiological data. The method also includes prompting the user to perform a first activity. The method also includes receiving second physiological data associated with the user, where the subsequent physiological data is associated with the user while the user is performing the first activity, after the user has performed the first activity, or both. The method also includes determining a subsequent drowsiness level of the user based at least in part on the second physiological data. The method also includes determining a first activity score based at least in part on the initial drowsiness level and the subsequent drowsiness level, where the first activity score indicates the effectiveness of the first activity in changing the user's drowsiness.

[0007] According to some implementations of the present disclosure, a system includes a memory storing machine-readable instructions and a control system including one or more processors configured to execute the machine-readable instructions to receive first physiological data associated with a user, where the first physiological data is generated by a sensor. The control system is further configured to determine an initial drowsiness level of the user based at least in part on the first physiological data. The control system is further configured to prompt the user to perform a first activity via an electronic device. The control system is further configured to receive second physiological data associated with the user, where the subsequent physiological data is generated by the sensor at least while the user is performing the first activity. The control system is further configured to determine a subsequent drowsiness level of the user based at least in part on the second physiological data. The control system is further configured to determine a first activity score based at least in part on the initial drowsiness level and the subsequent drowsiness level, where the first activity score indicates the effectiveness of the first activity in changing the user's drowsiness.

[0008] According to some implementations of the present disclosure, a method includes receiving physiological data associated with a user. The method also includes determining an initial drowsiness level of the user based at least in part on the physiological data. The method also includes prompting the user to perform a first activity using an electronic device. The method also includes, in response to the user performing a first portion of the first activity, determining a second drowsiness level of the user based at least in part on the physiological data. The method also includes changing one or more parameters of the first activity in a second portion of the first activity based at least in part on the initial drowsiness level, the second drowsiness level, or both.

[0009] According to some implementations of the present disclosure, a system includes an electronic interface, a memory, and a control system. The electronic interface is configured to receive physiological data associated with a user, the physiological data being generated by one or more sensors. The memory stores machine-readable instructions. The control system includes one or more processors configured to execute those machine-readable instructions to determine an initial drowsiness level of the user based at least in part on the physiological data. The control system is further configured to prompt the user to perform a first activity using an electronic device in response to the user performing a first portion of the first activity. The control system is further configured to determine a second drowsiness level of the user based at least in part on the physiological data. The control system is further configured to change one or more parameters of the first activity in a second portion of the first activity based at least in part on the initial drowsiness level, the second drowsiness level, or both.

[0010] According to some implementations of the present disclosure, a method includes receiving first physiological data associated with a user. The method also includes storing, in a user profile associated with the user, historical drowsiness data of the user including a set of historical changes in the user's drowsiness level, wherein each of the changes in the drowsiness level in the set of historical changes in the drowsiness level is associated with a corresponding one of a plurality of activities, and the historical drowsiness data is at least partially based on the first physiological data. The method also includes receiving second physiological data associated with the user. The method also includes determining an initial drowsiness level of the user at least partially based on the second physiological data generated by a second sensor. The method also includes training a machine learning algorithm using the user profile such that the machine learning algorithm is configured to (i) receive the initial drowsiness level of the user as an input and (ii) output one or more recommended activities from the plurality of activities to assist in changing the user's drowsiness level with respect to the initial drowsiness level.

[0011] According to some implementations of the present disclosure, a system includes an electronic interface, a memory, and a control system. The electronic interface is configured to receive (i) first physiological data associated with a user and (ii) second physiological data associated with the user, where the first physiological data and the second physiological data are generated by one or more sensors. The memory stores machine-readable instructions. The control system includes one or more processors configured to execute the machine-readable instructions to accumulate historical drowsiness data of the user, including changes in a set of the user's past drowsiness levels, in a user profile associated with the user, where each change in the drowsiness level in that set of past changes in the drowsiness level is associated with a corresponding one of a plurality of activities, and the historical drowsiness data is at least partially based on the first physiological data generated by a first sensor. The control system is further configured to determine an initial drowsiness level of the user at least partially based on the second physiological data generated by a second sensor. The control system is further configured to train a machine learning algorithm using the user profile such that the machine learning algorithm is configured to (i) receive the initial drowsiness level of the user as an input and (ii) output one or more recommended activities from a plurality of activities and determine to assist in changing the drowsiness level of the user with respect to the initial drowsiness level.

[0012] According to some implementations of the present disclosure, a method includes generating first physiological data associated with a user during a first period using a first sensor, and generating second physiological data associated with the user during the first period using a secondary sensor. The method also includes determining, by a control system, a first sleepiness level of the user based on the first physiological data generated by the first sensor. The method also includes determining, by the control system, a second sleepiness level of the user based on the second physiological data generated by the secondary sensor. The method also includes calibrating the secondary sensor such that the determined second sleepiness level matches the determined first sleepiness level.

[0013] According to some implementations of the present disclosure, a method includes receiving data associated with a user while the user is viewing a plurality of media content segments. The method also includes generating a score for each of the plurality of media content segments based at least in part on the received data, the score indicating a change in the user's sleepiness level. The method also includes determining the user's current sleepiness level. The method also includes recommending to the user at least one of the plurality of media content segments based at least in part on the user's current sleepiness level and the generated score to assist in changing the user's current sleepiness level.

[0014] According to some implementations of the present disclosure, a system includes a display device, a sensor, a memory, and a control system. The display device is configured to display a plurality of media content segments. The sensor is configured to generate data associated with a user while the user is viewing each of the plurality of media content segments. The memory stores machine-readable instructions. The control system includes one or more processors configured to execute the machine-readable instructions to generate a score for each of the plurality of media content segments based at least in part on the generated data associated with the user, the score indicating a change in the user's drowsiness level. The control system is further configured to determine the user's current drowsiness level. The control system is further configured to recommend one or more of the plurality of media content segments based at least in part on the user's current drowsiness level to assist in changing the user's current drowsiness level.

[0015] According to some implementations of the present disclosure, a system includes a display device configured to display media content, a sensor configured to generate data associated with a user while the user is viewing the media content, a memory storing machine-readable instructions, and a control system including one or more processors configured to execute the machine-readable instructions to accumulate a historical score for each of a plurality of segments of the media content based at least in part on the generated data associated with the user, the historical scores each indicating a change in the user's drowsiness level. The control system is further configured to determine the user's current drowsiness level. The control system is further configured to train a machine learning algorithm based at least in part on the historical scores such that the machine learning algorithm is configured to receive the user's current drowsiness level as input information and output one or more recommended segments from the plurality of segments of the media content to assist in changing the user's current drowsiness level.

[0016] The above summary is not intended to represent each implementation or each aspect of the present disclosure. Further features and advantages of the present disclosure will become apparent from the following detailed description and the drawings.

Brief Description of the Drawings

[0017]

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Modes for Carrying Out the Invention

[0018] While various modifications and alternative forms are possible, specific implementations and embodiments of the present disclosure are shown by way of example in the drawings and are detailed herein. However, it is not intended to limit the present disclosure to the specific forms disclosed, and it should be understood that the present disclosure covers all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.

[0019] Many people suffer from insomnia, a disorder generally characterized by dissatisfaction with the quality or duration of sleep (e.g., difficulty initiating sleep, frequent or long awakenings after initially falling asleep, and early awakening from which it is not possible to return to sleep). More than 2.6 billion people worldwide experience some form of insomnia, and it is estimated that more than 750 million people worldwide have received a diagnosis of a sleep disorder. In the United States, insomnia results in an estimated economic burden of $107.5 billion per year, accounting for 13.6% of all work-loss days and 4.6% of injuries requiring medical treatment. Also, recent research has shown that insomnia is the second most common mental disorder and that insomnia is the number one risk factor for depression.

[0020] Nocturnal insomnia symptoms generally include, for example, a decrease in sleep quality, a shortening of sleep duration, sleep initiation disorder, middle-of-the-night awakenings, late-onset insomnia, mixed insomnia, and / or paradoxical insomnia. Sleep initiation disorder is characterized by difficulty initiating sleep at bedtime. Middle-of-the-night awakenings are characterized by frequent and / or long awakenings during the night after the initial fall asleep. Late-onset insomnia is characterized by early morning awakenings (e.g., before the target or desired wake-up time) from which it is not possible to return to sleep. Coexisting insomnia refers to a type of insomnia in which the insomnia symptoms are caused, at least in part, by the symptoms or complications of another physical or mental condition (e.g., anxiety, depression, medical conditions, and / or drug use). Mixed insomnia refers to a combination of attributes of other types of insomnia (e.g., a combination of sleep initiation, sleep maintenance, and late insomnia symptoms). Paradoxical insomnia refers to a disconnect or mismatch between the quality of sleep perceived by the user and the actual quality of sleep of that user.

[0021] Symptoms of daytime (e.g., during the day) insomnia include, for example, fatigue, reduced energy, cognitive impairment (e.g., attention, concentration, and / or memory), functional difficulties in academic or occupational settings, and / or mood disorders. These symptoms can lead to psychological complications such as, for example, reduced performance, delayed reaction time, increased risk of depression, and / or increased risk of anxiety disorders. Insomnia symptoms can also lead to physiological complications such as, for example, reduced immune system function, high blood pressure, increased risk of heart disease, increased risk of diabetes, weight gain, and / or obesity.

[0022] Insomnia can also be classified based on its duration. For example, if the manifestation of insomnia symptoms subsides within less than 3 months, it is typically considered acute or transient. Conversely, if insomnia symptoms are manifested, for example, over a period of 3 months or more, it is typically considered chronic or persistent. Persistent / chronic insomnia symptoms often require a different treatment approach than acute / transient insomnia symptoms.

[0023] Mechanisms of insomnia include predisposing factors, precipitating factors, and perpetuating factors. Predisposing factors include excessive arousal, which is characterized by increased physiological arousal during sleep and wakefulness. Measures of excessive arousal include, for example, increased cortisol levels, increased autonomic nervous system activity (indicated by, for example, increased resting heart rate and / or heart rate variability), increased brain activity (e.g., increased EEG frequency during sleep and / or increased number of awakenings during REM sleep), increased metabolic rate, increased body temperature, and / or increased activity in the pituitary - adrenal axis. Precipitating factors include stressful life events (e.g., those related to employment or education, interpersonal relationships, etc.). Perpetuating factors include sleep deprivation and excessive worry about its consequences, which can cause insomnia symptoms to persist even after the exacerbating factors have been removed.

[0024] Once diagnosed, insomnia can be managed or treated using a variety of techniques and providing recommendations to the patient. As described herein, insomnia can be managed or treated by prompting the individual to perform one or more activities before bedtime to change (e.g., increase) the individual's sleepiness and assist the individual in falling asleep. Patients can generally be encouraged or recommended to generally practice healthy sleep habits (e.g., exercise regularly, be active during the day, have a routine, do not take naps, have dinner early, relax before bedtime, do not consume caffeine in the afternoon, avoid alcohol, make the bedroom comfortable, remove distractions from the bedroom, get out of bed if not sleepy, attempt to wake up at the same time every day regardless of bedtime) or be urged to avoid certain habits (e.g., working in bed, going to bed extremely early, going to bed when not tired). Persons suffering from insomnia can be treated by improving their sleep hygiene. Sleep hygiene generally refers to an individual's habits (e.g., diet, exercise, substance use, bedtime, pre-bedtime activities, activities in bed before bedtime, etc.) and / or environmental parameters (e.g., ambient light, ambient noise, ambient temperature, etc.). In at least some cases, an individual can improve sleep hygiene by going to bed at a specific bedtime every night, sleeping for a specific duration, waking up at a specific time, changing environmental parameters, or any combination thereof.

[0025] Examples of sleep-related disorders and / or breathing disorders include periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep-disordered breathing (SDB), obstructive sleep apnea (OSA), Cheyne-Stokes respiration (CSR), hypoventilation, obesity hypoventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD), and chest wall disorders. Obstructive sleep apnea (OSA) is a form of sleep-disordered breathing (SDB) characterized by events such as obstruction or blockage of the upper airway during sleep due to a combination of an abnormally small upper airway and a normal loss of muscle tone in the regions of the tongue, soft palate, and posterior pharyngeal wall. Cheyne-Stokes respiration (CSR) is another form of sleep-disordered breathing. CSR is a disorder of the patient's respiratory controller, with alternating periods of increasing and decreasing ventilation, known as the CSR cycle, occurring periodically. CSR is characterized by repeated deoxygenation and reoxygenation of arterial blood. Obesity hypoventilation syndrome (OHS) is defined as a combination of severe obesity and chronic hypercapnia during wakefulness in the absence of other clear causes of hypoventilation. Symptoms include dyspnea, headache upon waking, and excessive daytime sleepiness. Chronic obstructive pulmonary disease (COPD) encompasses any of a group of lower airway diseases that share certain characteristics, such as an increased resistance to air movement, prolongation of the expiratory phase of breathing, and loss of normal lung elasticity. Neuromuscular disease (NMD) encompasses a number of diseases and conditions that impair muscle function either directly through intrinsic muscle pathology or indirectly through neuropathy. Chest wall disorders are a group of chest wall deformities that cause ineffectiveness of the connection between the respiratory muscles and the chest wall.

[0026] These other disorders are characterized by specific events that occur during sleep (e.g., snoring, apnea, hypopnea, periodic limb movement, sleep disordered breathing, choking, increased heart rate, dyspnea, asthma attack, epileptic seizure, seizure, or any combination thereof). These other sleep-related disorders may have symptoms similar to insomnia, but distinguishing these other sleep-related disorders from insomnia is useful in customizing an effective treatment plan that differentiates features that may require various treatments. For example, while fatigue is generally a feature of insomnia, excessive daytime sleepiness is a feature specific to other disorders (e.g., OSA) and reflects a physiological tendency to fall asleep involuntarily.

[0027] Referring to FIG. 1, a system 100 according to some implementations of the present disclosure is shown. System 100 includes a control system 110, a memory device 114, an electronic interface 119, one or more sensors 130, and one or more user devices 170. In some implementations, system 100 further optionally includes a respiratory therapy system 120.

[0028] The control system 110 includes one or more processors 112 (hereinafter, the processor 112). The control system 110 is generally used to control (e.g., operate) various components of the system 100 and / or analyze data acquired and / or generated by the components of the system 100. The processor 112 can be a general-purpose or special-purpose processor or microprocessor. Although one processor 112 is shown in FIG. 1, the control system 110 can include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) that can be present within a single housing or located separately from each other. The control system 110 can be coupled to, and / or positioned within, for example, the housing of the user device 170 and / or one or more of the housings of the sensors 130. The control system 110 can be centralized (within one such housing) or distributed (within two or more such physically separate housings). In such an implementation including two or more housings that store the control system 110, such housings can be located in proximity to each other and / or remotely from each other.

[0029] The memory device 114 stores machine-readable instructions executable by the processor 112 of the control system 110. The memory device 114 can be any suitable computer-readable storage device or medium, such as, for example, a random or serial access memory device, a hard drive, a solid state drive, a flash memory device, etc. Although one memory device 114 is shown in FIG. 1, the system 100 can include any suitable number of memory devices 114 (e.g., 1 memory device, 2 memory devices, 5 memory devices, 10 memory devices, etc.). The memory device 114 can be coupled to and / or located within the housing of the respiratory therapy device 122, the housing of the user device 170, one or more of the housings of the sensor 130, or any combination thereof. Similar to the control system 110, the memory device 114 can be centralized (within one such housing) or distributed (within two or more physically different such housings).

[0030] In some implementations, the memory device 114 (FIG. 1) stores a user profile associated with a user. The user profile can include, for example, demographic information associated with the user, biometric information associated with the user, medical information associated with the user, self-reported user feedback, sleep parameters associated with the user (e.g., sleep-related parameters recorded from one or more previous sleep sessions), or any combination thereof. Demographic information can include, for example, information indicating the user's age, gender, race, family history of insomnia, or sleep apnea, the user's employment status, the user's education status, the user's socioeconomic status, or any combination thereof. Medical information can include, for example, information indicating one or more medical conditions associated with the user, medication use by the user, or both. The medical information data can further include the results or scores of a Multiple Sleep Latency Test (MSLT) and / or the scores or values of the Pittsburgh Sleep Quality Index (PSQI). Self-reported user feedback can include information indicating a self-reported subjective sleep score (poor, average, good, etc.), a self-reported subjective stress level of the user, a self-reported subjective fatigue level of the user, a self-reported subjective health state of the user, recent life events experienced by the user, or any combination thereof.

[0031] The electronic interface 119 is configured to receive data (e.g., physiological data) from one or more sensors 130, and the data can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The electronic interface 119 can communicate with one or more sensors 130 using a wired or wireless connection (e.g., using an RF communication protocol, a WiFi communication protocol, a Bluetooth® communication protocol, a cellular network, etc.). The electronic interface 119 can include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. The electronic interface 119 can also include another processor and / or another memory device that is the same or similar to the processor 112 and the memory device 114 described herein. In some implementations, the electronic interface 119 is coupled or integrated with the user device 170. In other implementations, the electronic interface 119 is coupled to or integrated with (e.g., within a housing) the control system 110 and / or the memory device 114.

[0032] As described above, in some implementations, system 100 optionally includes a respiratory therapy system 120. The respiratory therapy system 120 can include a respiratory pressure therapy device 122 (referred to herein as a respiratory therapy device 122), a user interface 124, a conduit 126 (also referred to as a tube or air circuit), a display device 128, a humidification tank 129, or a combination thereof. In some implementations, one or more of the control system 110, the memory device 114, the display device 128, the sensor 130, and the humidification tank 129 are part of the respiratory therapy device 122. Respiratory pressure therapy refers to applying an air supply at a controlled target pressure that is nominally positive relative to the atmosphere throughout the user's respiratory cycle (e.g., different from negative pressure therapies such as tank ventilators or cuirass ventilators). The respiratory therapy system 120 is generally used to treat individuals suffering from one or more sleep-related breathing disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea).

[0033] The respiratory therapy device 122 is generally used to generate pressurized air that is delivered to the user (e.g., using one or more motors that drive one or more compressors). In some implementations, the respiratory therapy device 122 generates a continuous and constant air pressure that is delivered to the user. In other implementations, the respiratory therapy device 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In still other implementations, the respiratory therapy device 122 is configured to generate various different air pressures within a predetermined range. For example, the respiratory therapy device 122 can deliver at least about 6 cmH2O, at least about 10 cmH2O, at least about 20 cmH2O, from about 6 cmH2O to about 10 cmH2O, from about 7 cmH2O to about 12 cmH2O, etc. The respiratory therapy device 122 can also deliver pressurized air at a predetermined flow rate between, for example, about -20 L / min and about 150 L / min while maintaining a positive pressure (relative to ambient pressure).

[0034] The user interface 124 engages with a portion of the user's face and delivers pressurized air from the respiratory therapy device 122 to the user's airway to assist in preventing the airway from narrowing and / or closing during sleep. This may also increase the user's oxygen uptake during sleep. Depending on the therapy applied, the user interface 124 may form a seal with, for example, an area or portion of the user's face, thereby facilitating the delivery of gas at a pressure that is sufficiently different from the ambient pressure, such as a positive pressure of about 10 cmH2O relative to the ambient pressure, to provide a therapeutic effect. In other forms of therapy, such as oxygen delivery, the user interface may not include a seal sufficient to facilitate the delivery of gas supply to the airway at a positive pressure of about 10 cmH2O.

[0035] As shown in FIG. 2, in some implementations, the user interface 124 is a face mask that covers the user's nose and mouth. Alternatively, the user interface 124 can be a nasal mask that provides air to the user's nose or a nasal pillow mask that delivers air directly to the user's nostrils. The user interface 124 can include a plurality of straps (e.g., including face fasteners) for positioning and / or stabilizing the interface on a portion of the user (e.g., the face), and a shape-conforming cushion (e.g., silicone, plastic, foam, etc.) that aids in providing an airtight seal between the user interface 124 and the user. The user interface 124 can also include one or more ventilation holes to allow carbon dioxide and other gases exhaled by the user 210 to escape. In other implementations, the user interface 124 includes a mouthpiece (e.g., a night guard mouthpiece shaped to fit the user's teeth, a mandibular repositioning device (MRD), etc.).

[0036] The conduit 126 (also referred to as an air circuit or tube) allows air to flow between two components of the respiratory therapy system 120, such as the respiratory therapy device 122 and the user interface 124. In some implementations, the conduit may have separate branches for inhalation and exhalation. In other implementations, a single-branch conduit is used for both inhalation and exhalation.

[0037] One or more of the respiratory therapy device 122, the user interface 124, the conduit 126, the display device 128, and the humidification tank 129 may include one or more sensors (e.g., a pressure sensor, a flow sensor, or more generally, any of the other sensors 130 described herein). These one or more sensors can be used, for example, to measure the air pressure and / or flow rate of the pressurized air supplied by the respiratory therapy device 122.

[0038] The display device 128 is generally used to display an image (singular or plural) including a still image, a video, or both, and / or information regarding the respiratory therapy device 122. For example, the display device 128 can provide information regarding the status of the respiratory therapy device 122 (e.g., whether the respiratory therapy device 122 is on or off, the pressure of the air delivered by the respiratory therapy device 122, the temperature of the air delivered by the respiratory therapy device 122, etc.) and / or other information (such as a sleep score and / or a therapy score, also referred to as myAir (registered trademark) score described in International Publication No. WO2016 / 061629, which is hereby incorporated by reference in its entirety, the current date / time, personal information of the user 210, etc.). In some implementations, the display device 128 functions as a human-machine interface (HMI) including a graphic user interface (GUI) configured to display an image (singular or plural) as an input interface. The display device 128 can be an LED display, an organic EL display, a liquid crystal display, or the like. The input interface can be, for example, a touch screen or a touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense an input made by a human user interacting with the respiratory therapy device 122.

[0039] The humidification tank 129 is connected or integrated with the respiratory therapy device 122 and includes a water reservoir that can be used to humidify the pressurized air delivered from the respiratory therapy device 122. The respiratory therapy device 122 can include a heater that heats the water in the humidification tank 129 to humidify the pressurized air provided to the user. Additionally, in some implementations, the conduit 126 can also include a heating element (e.g., one connected to and / or embedded in the conduit 126) that heats the pressurized air delivered to the user.

[0040] The respiratory therapy system 120 can be used, for example, as a positive airway pressure (PAP) system, a continuous positive airway pressure (CPAP) system, an auto positive airway pressure system (APAP), a biphasic or variable positive airway pressure system (BPAP or VPAP), a ventilator, or any combination thereof. The CPAP system delivers a predetermined air pressure (e.g., as determined by a sleep doctor) to the user. The APAP system automatically varies the air pressure delivered to the user, for example, based on respiratory data associated with the user. The BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., inspiratory positive airway pressure or IPAP) and a second predetermined pressure lower than the first predetermined pressure (e.g., expiratory positive airway pressure or EPAP).

[0041] Referring to FIG. 2, a portion of the system 100 (FIG. 1) according to some implementations is shown. The user 210 and the co - sleeper 220 of the respiratory therapy system 120 are in the bed 230 and lying on the mattress 232. The user interface 124 (e.g., a full - face mask) can be worn by the user 210 during a sleep session. The user interface 124 is fluidly connected and / or coupled to the respiratory therapy device 122 via the conduit 126. The respiratory therapy device 122 delivers pressurized air to the user 210 via the conduit 126 and the user interface 124 to increase the air pressure in the user 210's throat and assist in preventing the airway from closing and / or narrowing during sleep. The respiratory therapy device 122 can be positioned, as shown in FIG. 2, on the nightstand 240 directly adjacent to the bed 230, or more generally, on any surface or structure generally adjacent to the bed 230 and / or the user 210.

[0042] Referring again to FIG. 1, one or more sensors 130 of system 100 include a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, a radio frequency (RF) receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photoplethysmogram (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a moisture sensor 176, a LiDAR sensor 178, or any combination thereof. Generally, each of the one or more sensors 130 is configured to output sensor data received and stored in the memory device 114 or one or more other memory devices.

[0043] One or more sensors 130 are shown and described as including each of a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, an RF receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photoplethysmogram (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a moisture sensor 176, and a LiDAR sensor 178, but more generally, one or more sensors 130 can include any combination and any number of each of the sensors described and / or illustrated herein.

[0044] The physiological data generated by one or more of the sensors 130 can be used by the control system 110 to determine a sleep / wake signal and one or more sleep-related parameters associated with the user during a sleep session. The sleep / wake signal can indicate one or more sleep states including wakefulness, relaxed wakefulness, micro-awakenings, or sleep stages such as rapid eye movement (REM) stage, first non-REM stage (often referred to as "N1"), second non-REM stage (often referred to as "N2"), third non-REM stage (often referred to as "N3"), or any combination thereof. Methods for determining sleep states and / or sleep stages from physiological data generated by one or more sensors such as the sensor 130 are described, for example, in International Publication Nos. WO2014 / 047310, WO2017 / 132726, WO2019 / 122413, and WO2019 / 122414, and U.S. Patent Publication No. US2014 / 0088373, all of which are hereby incorporated by reference in their entirety.

[0045] The sleep / wake signal can also be provided with timestamps indicating, for example, the user's bedtime, the user's wake-up time, the user's sleep attempt time, etc. The sleep / wake signal can be measured by one or more sensors 130 at a predetermined sampling rate, such as 1 sample per second, 1 sample per 30 seconds, 1 sample per minute, etc., during a sleep session. In some implementations, the sleep / wake signal can also indicate a respiratory signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per hour, event pattern, pressure setting of the respiratory therapy device 122, or any combination thereof during the sleep session. Such event(s) can include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leak (e.g., from the user interface 124), restless legs, sleep disorder, choking, increased heart rate, dyspnea, asthma attack, epileptic seizure, seizure, or any combination thereof. One or more sleep-related parameters that can be determined for the user during the sleep session based on the sleep / wake signal include, for example, total time in bed, total sleep time, sleep latency, post-sleep arousal parameter, sleep efficiency, fragmentation index, or any combination thereof. In some implementations, one or more sleep-related parameters can include a sleep score as described in International Publication No. WO2015 / 006364, which is hereby incorporated by reference in its entirety.

[0046] The pressure sensor 132 outputs pressure data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the pressure sensor 132 is a pneumatic sensor (e.g., an atmospheric pressure sensor) that generates sensor data indicating the user's respiration (e.g., inhalation and / or exhalation) and / or ambient pressure of the respiratory therapy system 120. In such implementations, the pressure sensor 132 can be coupled or integrated with the respiratory therapy device 122. The pressure sensor 132 can be, for example, a capacitance sensor, an electromagnetic sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, a potentiometric sensor, or any combination thereof.

[0047] The flow sensor 134 outputs flow data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the flow sensor 134 is used to determine the air flow from the respiratory therapy device 122, the air flow through the conduit 126, the air flow through the user interface 124, or any combination thereof. In such implementations, the flow sensor 134 can be coupled or integrated with the respiratory therapy device 122, the user interface 124, or the conduit 126. The flow sensor 134 can be, for example, a mass flow sensor such as a rotary flow meter (e.g., a Hall effect flow meter), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot wire sensor, a vortex sensor, a membrane sensor, or any combination thereof.

[0048] The temperature sensor 136 outputs temperature data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the temperature sensor 136 generates temperature data indicating the core body temperature of the user 210 (FIG. 2), the skin temperature of the user 210, the temperature of the air flowing from and / or through the conduit 126 from the respiratory therapy device 122, the temperature within the user interface 124, the ambient temperature, or any combination thereof. The temperature sensor 136 can be, for example, a thermocouple sensor, a thermistor sensor, a silicon bandgap temperature sensor or a semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0049] Microphone 140 outputs sound data that can be stored in memory device 114 and / or analyzed by processor 112 of control system 110. Microphone 140 can be used to record sound(s) (e.g., sound from user 210) during a sleep session to determine one or more sleep-related parameters (e.g., using control system 110), as will be described in more detail herein. Microphone 140 can be coupled or integrated with respiratory therapy device 122, usage interface 124, conduit 126, or user device 170.

[0050] Speaker 142 outputs sound waves audible to a user of system 100 (e.g., user 210 of FIG. 2). Speaker 142 can be used, for example, as an alarm clock or for the purpose of playing an alert or message to user 210 (e.g., in response to an event). Speaker 142 can be coupled or integrated with respiratory therapy device 122, user interface 124, conduit 126, or user device 170.

[0051] The microphone 140 and the speaker 142 can be used as separate devices. In some implementations, the microphone 140 and the speaker 142 can be incorporated into an acoustic sensor 141 (e.g., a sonar sensor), for example, as described in WO2018 / 050913 and WO2020 / 104465, which are hereby incorporated by reference in their entireties. In such implementations, the speaker 142 generates or emits sound waves at a predetermined interval and / or frequency, and the microphone 140 detects the reflection of the sound waves emitted from the speaker 142. The sound waves generated or emitted by the speaker 142 have a frequency that is inaudible to the human ear (e.g., less than 20 Hz or greater than about 18 kHz) so as not to interfere with the sleep of the user 210 or the co-sleeper 220 (Figure 2). The control system 110 can determine one or more of the sleep-related parameters described herein, such as the location of the user 210 (Figure 2) and / or, for example, a respiratory signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per hour, event pattern, sleep state, pressure setting of the respiratory device 122, type of movement of the user (e.g., movement of one or more parts of the user, movement of the chest, etc.), pattern of movement by the user, number of movements by the user, or any combination thereof, based at least in part on data from the microphone 140 and / or the speaker 142.

[0052] An acoustic (e.g., sonar) sensor 141 can use passive and / or active acoustic sensing, such as by generating / transmitting ultrasonic or low-frequency ultrasonic sensing signals (e.g., within a frequency range of about 17 - 23 kHz, 18 - 22 kHz, or 17 - 18 kHz) in air. Such sonar sensors are described in relation to the above WO2018 / 050913 and WO2020 / 104465. The data generated by the acoustic (e.g., sonar) sensor 141 can indicate the user's breathing (e.g., respiratory rate, respiratory rate variability) and / or the user's movement, which can be used, for example, to determine the transition between the awake state and the non-REM sleep stage (N1 sleep). N1 sleep can be identified based at least in part on a decrease in respiratory rate variability (e.g., increased breathing regularity), a decrease in minute ventilation, and / or a decrease in the user's movement.

[0053] In some implementations, sensor 130 includes (i) a first microphone that is the same as or similar to microphone 140 and is integrated with acoustic sensor 141, and (ii) a second microphone that is the same as or similar to microphone 140 but is separate and distinct from the first microphone integrated with acoustic sensor 141.

[0054] The RF transmitter 148 generates and / or radiates radio waves having a predetermined frequency and / or a predetermined amplitude (e.g., within a high-frequency band, within a low-frequency band, long-wave signal, short-wave signal, etc.). The RF receiver 146 detects the reflection of the radio waves radiated from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine the position of the user 210 (FIG. 2) and / or one or more of the sleep-related parameters described herein. The RF receiver (either the RF receiver 146 and the RF transmitter 148 or another RF pair) can also be used for wireless communication between the control system 110, the breathing device 122, one or more sensors 130, the user device 170, or any combination thereof. Although the RF receiver 146 and the RF transmitter 148 are shown as separate and distinct elements in FIG. 1, in some implementations, the RF receiver 146 and the RF transmitter 148 are combined as part of an RF sensor 147 (e.g., a radar sensor). In some such implementations, the RF sensor 147 includes a control circuit. Specific forms of RF communication can be WiFi, Bluetooth®, etc.

[0055] In some implementations, the RF sensor 147 is part of a mesh system. An example of a mesh system is a WiFi mesh system, which can include mesh nodes, mesh router(s), and mesh gateway(s), each of which can be mobile / portable or stationary. In such an implementation, the WiFi mesh system includes a WiFi router and / or a WiFi controller, and one or more satellites (e.g., access points), each of which includes an RF sensor that is the same as or similar to the RF sensor 147. The WiFi router and the satellites communicate with each other constantly using WiFi signals. The WiFi mesh system can be used to generate motion data based on changes in the WiFi signals (e.g., differences in received signal strength) that occur between the router and the satellite(s) when an object or person moves and partially obstructs the signals. This motion data can indicate motion, breathing, heart rate, walking, falling, behavior, etc., or any combination thereof.

[0056] The camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, videos, thermal images, or combinations thereof) that can be stored in the memory device 114. The image data from the camera 150 can be used by the control system 110 to determine one or more of the sleep-related parameters described herein. For example, the image data from the camera 150 can be used to identify the user's position, determine the time when the user 210 enters the bed 230 (FIG. 2), and determine the time when the user 210 exits the bed 230.

[0057] The infrared (IR) sensor 152 outputs infrared image data that can be reproduced as one or more infrared images (e.g., still images, videos, or both) that can be stored in the memory device 114. The infrared data from the IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep session, including the temperature of the user 210 and / or the movement of the user 210. The IR sensor 152 can also be used in combination with the camera 150 when measuring the presence, position, and / or movement of the user 210. While the IR sensor 152 can detect infrared light having a wavelength between, for example, about 700 nm and about 1 mm, the camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.

[0058] The PPG sensor 154 outputs physiological data associated with the user 210 (FIG. 2) that can be used to determine one or more sleep-related parameters, such as, for example, heart rate, heart rate variability, cardiac cycle, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, estimated blood pressure parameter(s), or any combination thereof. The PPG sensor 154 can be worn by the user 210, embedded in clothing and / or fabric worn by the user 210, and / or embedded in and / or coupled to the user interface 124 and / or its associated headgear (e.g., straps, etc.).

[0059] The ECG sensor 156 outputs physiological data associated with the electrical activity of the heart of the user 210. In some implementations, the ECG sensor 156 includes one or more electrodes positioned on or around a portion of the user 210 during a sleep session. The physiological data from the ECG sensor 156 can be used to determine, for example, one or more of the sleep-related parameters described herein.

[0060] The EEG sensor 158 outputs physiological data associated with the electrical activity of the brain of the user 210. In some implementations, the EEG sensor 158 includes one or more electrodes positioned on or around the scalp of the user 210 during a sleep session. The physiological data from the EEG sensor 158 can be used, for example, to determine the sleep state of the user 210 at any given time during the sleep session. In some implementations, the EEG sensor 158 can be integrated into the user interface 124 and / or its associated headgear (such as straps, etc.).

[0061] The capacitance sensor 160, the force sensor 162, and the strain gauge sensor 164 output data that can be stored in the memory device 114 and used by the control system 110 to determine one or more of the sleep-related parameters described herein. The EMG sensor 166 outputs physiological data associated with the electrical activity generated by one or more muscles. The oxygen sensor 168 outputs oxygen data indicating the oxygen concentration of a gas (e.g., within the conduit 126 or in the user interface 124). The oxygen sensor 168 can be, for example, an ultrasonic oxygen sensor, an electro-chemical oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof. In some implementations, one or more of the sensors 130 also include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a blood pressure sensor, a oximetry sensor, or any combination thereof.

[0062] The analyte sensor 174 can be used to detect the presence of analytes in the exhaled breath of the user 210. The data output by the analyte sensor 174 can be stored in the memory device 114 and used by the control system 110 to determine the identity and concentration of any analytes contained in the breath of the user 210. In some implementations, the analyte sensor 174 is positioned near the mouth of the user 210 to detect analytes contained in the breath exhaled from the mouth of the user 210. For example, if the user interface 124 is a facial mask that covers the nose and mouth of the user 210, the analyte sensor 174 can be positioned within that facial mask to monitor the mouth breathing of the user 210. In other implementations, such as when the user interface 124 is a nasal mask or a nasal pillow mask, the analyte sensor 174 can be positioned near the nose of the user 210 to detect analytes contained in the breath exhaled through the nose of the user. In yet other implementations, when the user interface 124 is a nasal mask or a nasal pillow mask, the analyte sensor 174 can be positioned near the mouth of the user 210. In this implementation, the analyte sensor 174 can be used to detect whether air is inadvertently leaking from the mouth of the user 210. In some implementations, the analyte sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemicals or compounds. In some implementations, the analyte sensor 174 can also be used to detect whether the user 210 is breathing through the nose or the mouth. For example, if the presence of an analyte is detected by the data output by the analyte sensor 174 positioned near the mouth of the user 210 or (in implementations where the user interface 124 is a facial mask) within the facial mask, the control system 110 can use this data as an indication that the user 210 is breathing through the mouth.

[0063] The moisture sensor 176 outputs data that can be stored in the memory device 114 and used by the control system 110. The moisture sensor 176 can be used to detect moisture in various areas surrounding the user (e.g., inside the conduit 126 or the user interface 124, near the face of the user 210, near the connection between the conduit 126 and the user interface 124, near the connection between the conduit 126 and the respiratory therapy device 122, etc.). Thus, in some implementations, the moisture sensor 176 can be coupled or integrated with the user interface 124 or integrated into the conduit 126 to monitor the humidity of the pressurized air from the respiratory therapy device 122. In other implementations, the moisture sensor 176 is disposed near any area where it is necessary to monitor the moisture level. The moisture sensor 176 can also be used to monitor the humidity of the ambient environment surrounding the user 210, such as the air in the bedroom.

[0064] The optical detection and ranging (LiDAR) sensor 178 can be used for depth perception. This type of optical sensor (e.g., a laser sensor) can be used to detect objects and create a three-dimensional (3D) map of the surrounding environment such as the living space. LiDAR generally uses a pulsed laser to measure the time of flight. LiDAR is also commonly referred to as 3D laser scanning. In one example of the use of such a sensor, a fixed or mobile device (such as a smartphone) having a LiDAR sensor 166 can measure and map an area more than 5 meters away from the sensor. LiDAR data can be fused with, for example, point cloud data estimated by an electromagnetic RADAR sensor. The LiDAR sensor(s) 178 can also automatically create a geofence for the RADAR system by using artificial intelligence (AI) to detect and classify features in the space that can pose problems for the RADAR system, such as a glass window that may be highly reflective to RADAR. LiDAR can also be used to estimate changes in height that occur when a person sits down, falls, etc., in addition to the person's height. LiDAR can be used to form a 3D mesh representation of the environment. In a further application, the ability of LiDAR to reflect off solid surfaces through which radio waves pass (e.g., radio-transparent materials) enables the classification of different types of obstacles.

[0065] Although shown separately in FIG. 1, any combination of one or more sensors 130 can be integrated and / or coupled to any one or more of the components of system 100 including respiratory therapy device 122, user interface 124, conduit 126, humidification tank 129, control system 110, user device 170, or any combination thereof. For example, acoustic sensor 141 and / or RF sensor 147 can be integrated and / or coupled to user device 170. In such an implementation, user device 170 can be considered a secondary device that generates additional or secondary data used by system 100 (e.g., control system 110) according to some aspects of the present disclosure. In some implementations, at least one of the one or more sensors 130 is not coupled to respiratory therapy device 122, control system 110, or user device 170 and is positioned generally adjacent to user 210 during a sleep session (e.g., positioned on or in contact with a portion of user 210, worn by user 210, coupled to or positioned on a nightstand, coupled to a mattress, coupled to a ceiling, etc.).

[0066] The user device 170 (FIG. 1) includes a display device 172. The user device 170 can be, for example, a mobile device such as a smartphone, a tablet, a notebook computer, etc. Alternatively, the user device 170 can be an external sensing system, a television (e.g., a smart TV), or another smart home device (e.g., a smart speaker (singular or plural) such as Google Home, Amazon Echo, Alexa, etc.). In some implementations, this user device is a wearable device (e.g., a smartwatch). The display device 172 is generally used to display images (singular or plural) including still images, videos, or both. In some implementations, the display device 172 serves as a human-machine interface (HMI) including a graphic user interface (GUI) configured to display images (singular or plural) and an input interface. The display device 172 can be an LED display, an organic EL display, a liquid crystal display, etc. The input interface can be, for example, a touch screen or a touch-sensing substrate, a mouse, a keyboard, or any sensor system configured to sense an input made by a human user interacting with the user device 170. In some implementations, one or more user devices can be used by and / or included in the system 100.

[0067] In some implementations, system 100 also includes activity tracker 180. Activity tracker 180 is generally used to assist in generating physiological data associated with a user. Activity tracker 180 can include, for example, one or more of the sensors 130 described herein, such as motion sensor 138 (e.g., one or more accelerometers and / or gyroscopes), PPG sensor 154, and / or ECG sensor 156. Using the physiological data from activity tracker 180, for example, the number of steps, distance traveled, number of steps going uphill, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, average respiratory rate, resting respiratory rate, maximum heart rate, respiratory rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, calories burned, blood oxygen saturation, skin electrical activity (also referred to as skin conductance or galvanic skin response), or any combination thereof can be determined. In some implementations, activity tracker 180 is (e.g., electronically or physically) coupled to user device 170.

[0068] In some implementations, activity tracker 180 is a wearable device that a user can wear, such as a smartwatch, a wristband, a ring, or a patch. For example, referring to FIG. 2, activity tracker 180 is worn on the wrist of user 210. Activity tracker 180 can also be coupled or integrated with the clothing or apparel that the user wears. As a further alternative, activity tracker 180 can be coupled or integrated with user device 170 (e.g., within the same housing). It is more common for activity tracker 180 to be communicatively coupled or physically integrated (e.g., within a housing) with control system 110, memory 114, respiratory system 120, and / or user device 170.

[0069] Although the control system 110 and the memory device 114 are depicted and illustrated in FIG. 1 as separate and distinct components of the system 100, in some implementations, the control system 110 and / or the memory device 114 are integrated with the user device 170 and / or the respiratory therapy device 122. Alternatively, in some implementations, the control system 110 or a portion thereof (e.g., the processor 112) may be located in the cloud (e.g., integrated with a server, integrated with an Internet of Things (IoT) device, connected to the cloud, capable of receiving edge cloud processing, etc.) and may be located in one or more servers (e.g., a remote server, a local server, etc.), or any combination thereof.

[0070] The system 100 is shown as including all of the above components, but according to various implementations of the present disclosure, there may be more or fewer components that can be included in the system to generate physiological data and to determine recommended notifications or actions for the user. For example, a first alternative system includes the control system 110, the memory device 114, and at least one of the one or more sensors 130. As another example, a second alternative system includes the control system 110, the memory device 114, at least one of the one or more sensors 130, and the user device 170. As yet another example, a third alternative system includes the control system 110, the memory device 114, the respiratory therapy system 120, at least one of the one or more sensors 130, and the user device 170. Thus, various systems can be formed using any part(s) of the components shown and described herein and / or in combination with one or more other components.

[0071] In this specification, a sleep session can be defined in multiple ways. For example, a sleep session can be defined by an initial start time and an end time. In some implementations, a sleep session is the duration during which the user is asleep, that is, the sleep session has a start time and an end time, and the user does not wake up until the end time during the sleep session. That is, any period during which the user is awake is not included in the sleep session. From the first definition of the sleep session, if the user wakes up and falls asleep multiple times in one night, each sleep period separated by those wake periods becomes a sleep session.

[0072] Alternatively, in some implementations, a sleep session has a start time and an end time, and during that sleep session, the user can wake up without the sleep session ending as long as the continuous duration during which the user is awake is less than the wake duration threshold. The wake duration threshold can be defined as a percentage of the sleep session. The wake duration threshold can be, for example, about 20 percent of the sleep session, about 15 percent of the sleep session duration, about 10 percent of the sleep session duration, about 5 percent of the sleep session duration, about 2 percent of the sleep session duration, etc., or any other arbitrary threshold percentage. In some implementations, the wake duration threshold is defined as, for example, about 1 hour, about 30 minutes, about 15 minutes, about 10 minutes, about 5 minutes, about 2 minutes, etc., or any other arbitrary amount of time.

[0073] In some implementations, a sleep session is defined as the total time from the time of night when the user first goes to bed to the time of the next morning when the user last wakes up. In other words, the sleep session starts at the first time (e.g., 10:00 PM) of the first date (e.g., Monday, January 6, 2020), which is called the current night, when the user first goes to bed with the intention of sleeping (not when the user first intends to watch TV or fiddle with a smartphone before going to bed), and ends at the second time (e.g., 7:00 AM) of the second date (e.g., Tuesday, January 7, 2020), which is called the next morning, when the user first wakes up with the intention of not going back to sleep again.

[0074] Referring to FIG. 3, an exemplary time series 300 of a sleep session is shown. The time series 300 includes a bedtime (t bed ), a sleep onset time (t GTS ), a first sleep time (t sleep ), a first microarousal MA1 and a second microarousal MA2, an arousal A, a wake-up time (t wake ), and a wake-up time (t rise ).

[0075] The bedtime t bed is associated with the time when the user first goes to bed (e.g., the bed 230 in FIG. 2) (e.g., the user lies down or sits on the bed) before falling asleep. The bedtime t bed can be specified based on a bedtime threshold duration to distinguish between the time when the user goes to bed to sleep and the time when the user goes to bed for other reasons (e.g., to watch TV). For example, the bedtime threshold duration can be at least about 10 minutes, at least about 20 minutes, at least about 30 minutes, at least about 45 minutes, at least about 1 hour, at least about 2 hours, etc. In this specification, the bedtime t bed is described with reference to the bed, but more generally, the bedtime t bed can represent the time when the user first takes a seat on something (e.g., a sofa, a chair, a sleeping bag, etc.) to sleep.

[0076] The sleep onset time (GTS) is associated with the time (t bed ) when the user first attempts to fall asleep after getting into bed. For example, after getting into bed and before trying to sleep, the user may engage in one or more activities (such as reading, watching TV, listening to music, using the user device 170, etc.) to relax. The first sleep time (t sleep ) is the time when the user first falls asleep. For example, the first sleep time (t sleep ) may be the time when the user first enters the non-REM sleep stage.

[0077] The wake-up time t wake is the time associated with the period when the user wakes up without returning to sleep (e.g., rather than waking up in the middle of the night and going back to sleep). After initially falling asleep, the user may experience one of more unconscious micro-awakenings (such as micro-awakenings MA1 and MA2) with a short duration (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.). The user returns to sleep after each of the micro-awakenings MA1 and MA2 rather than at the wake-up time t wake . Similarly, after initially falling asleep, the user may have one or more conscious awakenings (such as awakening A) (e.g., getting up to go to the toilet, taking care of a child or pet, sleepwalking, etc.). However, the user returns to sleep after awakening A. Therefore, the wake-up time t wake can be defined, for example, by a wake-up threshold duration (e.g., the user is awake for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.).

[0078] Similarly, the wake-up time t rise is associated with the time when the user leaves and departs from the bed with the intention of ending the sleep session (e.g., not going to the toilet in the middle of the night, taking care of a child or pet, sleepwalking, etc.). In other words, the wake-up time t rise is the time when the user last leaves the bed without returning to the bed until the next sleep session (e.g., the next night). Therefore, the wake-up time trise can be defined, for example, based on a getting-up threshold duration (e.g., the user is out of bed for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.). The bedtime t of the second subsequent sleep session bed can also be defined based on a getting-up threshold duration (e.g., the user is out of bed for at least 4 hours, at least 6 hours, at least 8 hours, at least 12 hours, etc.).

[0079] As described above, the user can wake up and get out of bed one more time during the night from the first t bed to the final t rise In some implementations, the final wake-up time t wake and / or the final getting-up time t rise that are identified or determined based on a predetermined threshold duration after an event (e.g., going to sleep or getting out of bed). Such a threshold duration can be customized according to the user. For a standard user who goes to bed at night and wakes up and gets out of bed in the morning, any period between about 12 hours and about 18 hours (the period from when the user wakes up (t wake ) or gets out of bed (t rise ) until going to bed (t bed ), falling asleep (t GTS ) or going to sleep (t sleep )) can be used. For a user who spends a long time in bed, a shorter threshold period (e.g., about 8 hours to about 14 hours) can be used. This threshold period can be initially selected and / or adjusted later based on a system that monitors the user's sleep behavior.

[0080] The total in-bed time (TIB) is from the bedtime t bed to the getting-up time t riseis the duration up to. Total sleep time (TST) is associated with the time from the initial sleep time to the waking time, excluding any conscious or unconscious awakenings and / or micro-awakenings during that period. Total sleep time (TST) is generally shorter than total in-bed time (TIB) (e.g., 1 minute shorter, 10 minutes shorter, 1 hour shorter, etc.). For example, referring to the time series 300 in FIG. 3, the total sleep time (TST) is from the initial sleep time t sleep to the waking time t wake but the durations of the first micro-awakening MA1, the second micro-awakening MA2, and the awakening A are excluded. As shown, in this embodiment, the total sleep time (TST) is shorter than the total in-bed time (TIB).

[0081] In some implementations, the total sleep time (TST) can be defined as the total persistent sleep time (PTST). In such implementations, the total persistent sleep time excludes a predetermined first portion or first period of the first non-REM stage (e.g., the light sleep stage). For example, this predetermined first portion can be from about 30 seconds to about 20 minutes, from about 1 minute to about 10 minutes, from about 3 minutes to about 5 minutes, etc. The total persistent sleep time is a measure of continuous sleep and smooths the sleep / wake sleep profile. For example, at the user's initial sleep onset, the user may enter the first non-REM stage for a very short time (e.g., about 30 seconds), return to the wake stage for a short period (e.g., 1 minute), and then return to the first non-REM stage. In this example, the total persistent sleep time excludes the first instance of the first non-REM stage (e.g., about 30 seconds).

[0082] In some implementations, the sleep session is defined as the time starting at the in-bed time (t bed ) and ending at the wake-up time (t rise ), i.e., the total in-bed time (TIB). In some implementations, the sleep session starts at the initial sleep time (t sleep ) and ends at the waking time (t wake) is defined as that which ends at. In some implementations, the sleep session is defined as the total sleep time (TST). In some implementations, the sleep session GTS ) starts at the time of falling asleep (t wake ) and is defined as that which ends at the time of waking up (t GTS ). In some implementations, the sleep session starts at the time of falling asleep (t rise ) and is defined as that which ends at the time of getting up (t bed ). In some implementations, the sleep session starts at the time of going to bed (t wake ) and is defined as that which ends at the time of waking up (t sleep ). In some implementations, the sleep session starts at the first sleep time (t rise ) and is defined as that which ends at the time of getting up (t

[0083] Referring to FIG. 4, an exemplary sleep profile 400 corresponding to the time series 300 (FIG. 3) according to some implementations is shown. As shown, the sleep profile 400 includes a sleep / wake signal 401, a wake stage axis 410, a REM stage axis 420, a light sleep stage axis 430, and a deep sleep stage axis 440. The intersection of the sleep / wake signal 401 and one of the axes 410-440 indicates the sleep stage at any given time during the sleep session.

[0084] The sleep / wake signal 401 can be generated based on physiological data associated with a user (e.g., generated by one or more of the sensors 130 described herein). The sleep / wake signal can indicate one or more sleep states including wakefulness, relaxed wakefulness, micro-awakening, REM stage, first non-REM stage, second non-REM stage, third non-REM stage, or any combination thereof. In some implementations, one or more of the first non-REM stage, the second non-REM stage, and the third non-REM stage can be grouped together and classified as a light sleep stage or a deep sleep stage. For example, the light sleep stage can include the first non-REM stage, and the deep sleep stage can include the second non-REM stage and the third non-REM stage. The hypnogram 400 is shown in FIG. 4 as including a light sleep stage axis 430 and a deep sleep stage axis 440, but in some implementations, the hypnogram 400 can include axes representing each of the first non-REM stage, the second non-REM stage, and the third non-REM stage. In other implementations, the sleep / wake signal can also indicate a respiration signal, respiratory rate, inhalation amplitude, exhalation amplitude, inhalation-to-exhalation ratio, number of events per hour, event pattern, or any combination thereof. The information describing the sleep / wake signal can be stored in the memory device 114.

[0085] The hypnogram 400 can be used to determine one or more sleep-related parameters such as, for example, sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), sleep fragmentation index, sleep block, or any combination thereof.

[0086] The sleep onset latency (SOL) is the time at sleep onset (t GTS ) and the first sleep time (t sleep) and is defined as the time between. In other words, the sleep onset latency indicates the time required for the user to actually fall asleep after first attempting to fall asleep. In some implementations, the sleep onset latency is defined as the persistent sleep onset latency (PSOL). The persistent sleep onset latency differs from the sleep onset latency in that it is defined as the duration from the sleep onset time to a predetermined amount of persistent sleep. In some implementations, the predetermined amount of persistent sleep can include, for example, within the second non-REM stage, within the third non-REM stage, and / or at least 10 minutes of sleep within a REM stage with awakenings of 2 minutes or less, the first non-REM stage, and / or movement therebetween. In other words, the persistent sleep onset latency requires, for example, up to 8 minutes of persistent sleep within the second non-REM stage, within the third non-REM stage, and / or within the REM stage. In other implementations, the predetermined amount of persistent sleep can include at least 10 minutes of sleep within the first non-REM stage, within the second non-REM stage, within the third non-REM stage, and / or within the REM stage after the initial sleep time. In such implementations, the predetermined amount of persistent sleep can exclude any micro-awakenings (e.g., after a 10-second micro-awakening, the 10 minutes are not restarted).

[0087] Wake after sleep onset (WASO) is associated with the total duration that the user is awake between the initial sleep time and the wake time. Therefore, wake after sleep onset includes short awakenings and micro-awakenings (e.g., micro-awakenings MA1 and MA2 shown in FIG. 4) during the sleep session, whether conscious or unconscious. In some implementations, wake after sleep onset (WASO) is defined as persistent wake after sleep onset (PWASO) that includes only the total wake durations having a predetermined length (e.g., greater than 10 seconds, greater than 30 seconds, greater than 60 seconds, greater than about 5 minutes, greater than about 10 minutes, etc.).

[0088] Sleep efficiency (SE) is determined as the ratio of total in-bed time (TIB) to total sleep time (TST). For example, if the total in-bed time is 8 hours and the total sleep time is 7.5 hours, the sleep efficiency of that sleep session is 93.75%. Sleep efficiency represents the user's sleep hygiene. For example, if the user goes to bed and spends time on other activities (such as watching TV) before sleep, the sleep efficiency will decrease (for example, the user will receive a penalty). In some implementations, the sleep efficiency (SE) can be calculated based on the total in-bed time (TIB) and the total time the user attempts to fall asleep. In such implementations, the total duration that the user attempts to fall asleep is defined as the time from the lights-out to sleep (GTS) time described herein to the wake-up time. For example, if the total sleep time is 8 hours (for example, from 11 PM to 7 AM), the lights-out to sleep time is 10:45 PM, and the wake-up time is 7:15 AM, in such implementations, the sleep efficiency parameter is calculated to be approximately 94%.

[0089] The fragmentation index is determined at least in part based on the number of awakenings during a sleep session. For example, if the user has two micro-awakenings (such as micro-awakenings MA1 and MA2 shown in FIG. 4), the fragmentation index can be represented as 2. In some implementations, this fragmentation index is scaled between integers in a predetermined range (for example, between 0 and 10).

[0090] A sleep block is associated with a transition between any sleep stage (such as the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or REM) and an awake stage. A sleep block can be calculated, for example, with a resolution of 30 seconds.

[0091] In some implementations, the systems and methods described herein use the time of going to bed (t bed ), the time of lights-out to sleep (t GTS ), the first sleep time (t sleep ), one or more first micro-awakenings (such as MA1 and MA2), the wake-up time (t wake ), and the time of getting up (t rise) or any combination thereof, generating or analyzing a sleep profile including a sleep / wake signal to determine or identify at least partially based on the sleep / wake signal of the sleep profile.

[0092] In other implementations, one or more of the sensors 130 are used to determine the time of going to bed (t bed ), the time of falling asleep (t GTS ), the first sleep time (t sleep ), one or more first micro-awakenings (e.g., MA1 and MA2), the awakening time (t wake ), the waking-up time (t rise ), or any combination thereof, and thus define a sleep session. For example, the time of going to bed t bed can be determined based on data generated by the motion sensor 138, the microphone 140, the camera 150, or any combination thereof. The time of falling asleep can be determined, for example, based on data from the motion sensor 138 (e.g., data indicating no movement by the user), data from the camera 150 (e.g., data indicating no movement by the user and / or data indicating that the user turned off the lighting), data from the microphone 140 (e.g., data indicating that the user turned off the TV), data from the user device 170 (e.g., data indicating that the user is no longer using the user device 170), data from the pressure sensor 132 and / or the flow sensor 134 (e.g., data indicating that the user turned on the power of the respiratory therapy device 122, data indicating that the user put on the user interface 124), or any combination thereof.

[0093] Referring to FIG. 5, a method 500 for determining an activity score indicating the effectiveness of an activity in changing (e.g., enhancing) a user's sleepiness is shown. One or more steps of the method 500 can be implemented using any element or aspect of the system 100 (FIGS. 1-2) described herein.

[0094] Step 501 of method 500 includes generating and / or receiving initial physiological data associated with a user. The initial physiological data can be received, for example, by the electronic interface 119 (FIG. 1) described herein. The initial physiological data can be generated or obtained by at least one of the one or more sensors 130 (FIG. 1). For example, in some implementations, the initial physiological data is generated using the above-described acoustic sensor 141 or RF sensor 147 coupled or integrated with the user device 170. In other implementations, the initial physiological data is generated or obtained using the pressure sensor 132 and / or the flow sensor 134 (FIG. 1) coupled or integrated with the respiratory therapy device 122. Information describing the initial physiological data received in step 501 can be stored in the memory device 114 (FIG. 1).

[0095] Step 502 of method 500 includes determining (step 501) an initial drowsiness level of the user based at least in part on the initial physiological data. For example, the control system 110 can analyze the initial physiological data (stored, for example, in the memory device 114) to determine the initial drowsiness level of the user. Information describing the initial drowsiness level can be stored, for example, in the memory device 114 (FIG. 1).

[0096] As used herein, the sleepiness level generally indicates the user's fatigue, drowsiness, alertness, and / or awareness, and more generally indicates how close the user is to falling asleep. The sleepiness level can be determined and / or expressed in various ways. The sleepiness level can be, for example, a scaled value within a predetermined range (e.g., between 1 and 10) where the maximum value indicates very sleepy and the minimum value indicates not sleepy (or vice versa). Alternatively, the sleepiness level can be expressed using subjective descriptors (e.g., extremely sleepy, very sleepy, sleepy, neutral, awake, very awake, extremely awake, unresponsive, concentrated, fatigued, etc.). Other examples for representing the sleepiness level include using the Epworth Sleepiness Scale, the Stanford Sleepiness Scale, the Karolinska Sleepiness Scale, etc. In the Multiple Sleep Latency Test (MSLT) and the Maintenance of Wakefulness Test (MWT), objective scales are used to quantify sleepiness. The Oxford Sleep Resistance (OSLER) test, which is a simplified version of the MWT, is another objective test that can be used to indirectly quantify sleepiness. The Epworth Sleepiness Scale and the Stanford Sleepiness Scale subjectively quantify sleepiness.

[0097] The sleepiness level can be determined based on various types of data or combinations of data. In some implementations, the user's sleepiness level can be determined at least in part based on physiological data associated with the user. For example, the sleepiness level can be based on the respiratory rate, respiratory rate variability, respiratory stability (e.g., the consistency of the shape and / or speed of each breath, the mean squared difference between consecutive breaths), the change in tidal volume (e.g., estimated from the amplitude of the breath), movements associated with the user (e.g., physical movement of any part of the user's body, movement of the user's chest, etc.), eye blinks (e.g., the number of eye blinks per minute, the average of eye blinks per minute, etc.), and the duration of one or more of these parameters (e.g., the sleepiness level can increase with the prolongation of the duration of a formed predetermined breath, but decrease when movement of a part of the user's body is detected (e.g., when the user gets up in bed and sits up)).

[0098] The drowsiness level can also be determined using algorithms or statistical models such as, for example, linear regression algorithms, logistic regression algorithms, machine learning algorithms (e.g., trained machine learning algorithms with or without a teacher), support vector machine algorithms (SVM), decision trees, etc. For example, a certain model (e.g., a machine learning algorithm) can receive input data (e.g., physiological data, subjective feedback, scores from any of the objective drowsiness tests described herein), and be trained to predict the drowsiness level according to one or more of the objective tests described herein (e.g., Epworth, Stanford, Karolinska, MSLT, MWT, OSLER, etc.). Such a model can also predict the drowsiness level using demographic information associated with the user. As an example, women generally have a lower tidal volume (e.g., compared to men), and people with a higher body mass index (BMI) may have a higher tidal volume. Other data that can be used to predict the drowsiness level include the user's typical sleep schedule, day of the week, the user's work schedule, whether the user has been diagnosed with insomnia and / or has symptoms of insomnia, the level of activity or exercise, caffeine and / or alcohol intake, etc. In some cases, the predicted drowsiness level may not match the subjective drowsiness level reported by the user. In such cases, the model (e.g., a machine learning algorithm) for determining the drowsiness level can be modified or adjusted for each user.

[0099] In one embodiment, the drowsiness level can be determined based on physiological data from the EEG sensor 158 (FIG. 1) described herein. In another embodiment, the drowsiness level can be determined using data from the camera 150 (FIG. 1) to determine one or more properties of the user's eye(s) indicative of drowsiness (e.g., measuring vertical eye opening or eye height, eye opening, eye closing, blinking, eye movement, pupil dilation, etc.). In yet another embodiment, the drowsiness data can be determined based on the user's heart rate, the user's heart rate variability, the user's respiratory rate, the user's respiratory rate variability, the user's body temperature, or any combination thereof.

[0100] In some implementations, step 502 includes receiving subjective feedback from the user and determining an initial drowsiness level based at least in part on the subjective feedback. The subjective feedback can include, for example, a self-reported subjective drowsiness level (e.g., tired, sleepy, average, neutral, awake, at rest, etc.). Information associated with or indicative of the feedback from the user can be received, for example, through the user device 170 (e.g., via alphanumeric text, voice-to-text conversion, etc.). In some implementations, method 500 includes prompting the user to provide feedback for step 502. For example, the control system 110 can cause one or more prompts to be displayed on the display device 172 of the user device 170 (FIG. 1) to provide an interface for the user to provide feedback (e.g., the user clicks or taps to enter feedback, the user uses an alphanumeric keyboard to enter feedback). The received user feedback can be stored, for example, in the memory device 114 (FIG. 1) described herein.

[0101] In some implementations of method 500, memory device 114 (FIG. 1) stores a user profile associated with a user. The user profile can include, for example, demographic information associated with the user, biometric information associated with the user, medical information associated with the user, self-reported user feedback, sleep parameters associated with the user (e.g., sleep-related parameters recorded from one or more previous sleep sessions), or any combination thereof. Demographic information can include information indicating, for example, the user's age, gender, race, family history of insomnia, employment status of the user, educational status of the user, socioeconomic status of the user, or any combination thereof. Medical information can include, for example, one or more medical conditions associated with the user, drug use by the user, or both. The medical information data can further include the test results or scores of a Multiple Sleep Latency Test (MSLT) and / or the scores or values of a Pittsburgh Sleep Quality Index (PSQI). Self-reported user feedback can include information indicating a self-reported subjective sleep score (poor, average, good, etc.), a self-reported subjective stress level of the user, a self-reported subjective fatigue level of the user, a self-reported subjective health status of the user, recent life events experienced by the user, or any combination thereof.

[0102] Step 503 of method 500 includes prompting the user to perform a first activity. In some implementations, for example, the user can be prompted via the user device 170 (e.g., via text, audio, or both). In such implementations, the control system 110 can cause the user device 170 to prompt the user to perform a first activity. The first activity can be, for example, a mental activity, a physical activity, reading, a game, a puzzle, viewing media content (e.g., video, movie, TV show, etc.), listening to audio content (e.g., an audiobook), exercise, movement (e.g., movement that consumes energy, movement that induces fatigue, etc.), yoga, meditation, breathing exercises, consumption of a beverage (e.g., warm milk), or any combination thereof. In some implementations, the user device 170 also provides or facilitates the first activity (e.g., via the display 172). The type / nature of the first activity can be selected, for example, based on the user's initial drowsiness and the likelihood that the first activity will change (e.g., increase) the user's drowsiness.

[0103] Step 504 of method 500 includes receiving subsequent physiological data associated with the user in the same or a similar manner as the first physiological data received during the execution of step 501. The subsequent physiological data (step 504) may differ from the first physiological data (step 501) in that the subsequent physiological data is generated or acquired during at least a portion of the first activity, during the entire first activity, after the user has performed the first activity, or any combination thereof. For example, subsequent physiological data can be generated during the entire first activity or a portion of the first activity (e.g., at least 10% of the first activity, at least 30% of the first activity, at least 50% of the first activity, at least 75% of the first activity, at least 90% of the first activity, etc.). The subsequent physiological data can also be generated or acquired after the completion of the first activity (e.g., within about 30 seconds after the completion of the first activity, within about 1 minute after the completion of the first activity, within about 3 minutes after the completion of the first activity, within about 10 minutes after the completion of the first activity, within about 30 minutes after the completion of the first activity, etc.).

[0104] Step 505 of method 500 includes determining (step 504) the subsequent drowsiness level of the user based at least in part on the subsequent physiological data. The subsequent drowsiness level can be determined (step 502) in the same or a similar manner as the first drowsiness level described herein. The subsequent drowsiness level may differ from the first drowsiness level in that the subsequent drowsiness level indicates the drowsiness of the user after performing the first activity. The subsequent drowsiness level can, for example, exceed the first drowsiness level in at least some cases (the user may be more fatigued, for example, after performing the first activity).

[0105] In some implementations, step 505 also includes receiving subjective feedback from the user and determining a subsequent drowsiness level based at least in part on the subjective feedback. The subjective feedback may include, for example, a self-reported subjective drowsiness level (e.g., tired, sleepy, average, neutral, awake, resting, etc.). Information associated with or indicative of the feedback from the user can be received, for example, through user device 170 (e.g., via alphanumeric text, voice-to-text conversion, etc.). In some implementations, method 500 includes prompting the user to provide feedback for step 505. For example, control system 110 can cause one or more prompts to be displayed on display device 172 of user device 170 (FIG. 1) that provides an interface for the user to provide feedback (e.g., the user clicks or taps to enter feedback, the user uses an alphanumeric keyboard to enter feedback). The received user feedback can be stored, for example, in memory device 114 (FIG. 1) described herein.

[0106] Step 505 of method 500 includes determining a first activity score indicative of the effectiveness of a first activity in changing (e.g., enhancing) the user's drowsiness, based at least in part on the initial drowsiness level (step 502), subsequent drowsiness levels (step 504), or both. The first activity score can be, for example, a numerical value on a predetermined scale (e.g., between 1 and 10, between 1 and 100, etc.), a letter grade (e.g., A, B, C, D, or F), or a descriptor (e.g., poor, fair, good, excellent, average, below average, etc.). The numerical value, letter grade, or descriptor is assigned to the activity and represents an activity score based on how effective the activity is in inducing and / or enhancing the user's drowsiness. This numerical value, letter grade, or descriptor can correspond to an activity that is effective or ineffective in inducing and / or enhancing drowsiness, or a gradual effectiveness with respect to inducing and / or enhancing drowsiness, which can be an absolute value (e.g., of a predetermined scale) assigned to the activity or a relative value assigned to the activity with respect to other activities (e.g., other activities the user has initiated or can initiate). The first activity score is then typically associated with the first activity (e.g., within memory device 114) to reflect how effective the first activity was in increasing the user's drowsiness.

[0107] In some implementations, the first activity score is determined based at least in part on the difference between the initial drowsiness level (step 502) and a subsequent drowsiness level (step 505). For example, if the initial drowsiness level is 5 on a scale of 1 to 10 and the subsequent drowsiness level is 9 on a scale of 1 to 10, the first activity score can be determined to be 4 based on that difference. In some implementations, the first activity score can be determined based at least in part on the rate of change between the initial drowsiness level (step 502) and a subsequent drowsiness level (step 505). In other implementations, the first activity score can be determined based at least in part on a predetermined threshold. The first activity score can be determined based at least on, for example, the difference between the first time (step 502) associated with the initial drowsiness level and a second time associated with a subsequent drowsiness level that exceeds the predetermined threshold.

[0108] In some implementations, method 500 includes prompting the user to perform a second activity in the same or a similar manner as described in step 503. In some implementations, the second activity is different from the first activity. The second activity may have, for example, a different complexity than the first activity (e.g., the second activity is less complex), a different duration than the first activity (e.g., the second activity is longer than the first activity), or both. Generally, the complexity of an activity can be associated with, for example, the level of attention required for the user to perform the activity, the level of input required for the user to perform the activity, the number of actions required for the user to perform the activity, the length of the activity, the duration of the activity, the speed of the activity, or any combination thereof. The complexity of an activity can be expressed in subjective terms (e.g., very difficult, difficult, easy, very easy, non-trivial, trivial, average, below average, above average, etc.). Alternatively, the complexity of an activity can be expressed as a value within a predetermined range (e.g., between 1 and 10 where 1 is the simplest and 10 is the most complex or vice versa). In some implementations, the second activity is associated with a second activity score and is selected based on the first activity score (step 506) (e.g., the second activity score associated with the second activity is higher than the first activity score determined for the first activity).

[0109] In some implementations, method 500 includes receiving information indicative of a user's reaction to a given stimulus. For example, method 500 may include providing a stimulus using a standardized test such as, for example, a click reaction time test, a tap reaction time test, a speed test, a recognition test, a solution test, a processing test, a decoding test, a bar reaction test, an electro-optical panel reaction test, or any combination thereof, and receiving information (e.g., reaction time and / or reaction accuracy) indicative of the user's reaction to the stimulus. In another example, the stimulus is generated by a light source (e.g., display 172 of user device 170 described herein). In a further example, the stimulus is generated by a speaker (e.g., speaker 142 described herein). In such implementations, step 505 includes determining a first activity score based at least in part on information indicative of the user's reaction to the provided stimulus. The information indicative of the user's reaction may include reaction time, reaction accuracy, or both. Information describing reaction time can be used to determine the initial drowsiness level (step 502) and / or subsequent drowsiness levels (step 505).

[0110] In some implementations, method 500 includes verifying a determined first activity score based at least in part on additional physiological data generated during a user's sleep session following a first activity. In such implementations, method 500 includes receiving sleep physiological data associated with at least a portion of the user's sleep session (e.g., generated by any combination of sensors 130 described herein). The method may also include determining one or more of the sleep-related parameters described herein associated with the sleep session, based at least in part on the sleep physiological data. Thereafter, the determined first activity score can be verified or validated using the one or more sleep-related parameters (step 506). For example, the method may include determining a latency to sleep parameter for the sleep session following the first activity. If the sleep parameter is less than a predetermined threshold (e.g., 15 minutes, 20 minutes, 30 minutes, etc.) and the first activity score indicates that the first activity is effective in promoting the user's drowsiness, then the sleep parameter can confirm that the first activity is actually effective in promoting the user's drowsiness.

[0111] One or more of the steps of method 500 described herein can be repeated one or more times for additional activities (e.g., second, third, tenth activity sessions, etc.).

[0112] Referring to FIG. 6, a method of changing one or more parameters of an activity to increase a user's drowsiness is shown. One or more steps of method 600 can be implemented using any element or aspect of system 100 (FIGS. 1-2) described herein.

[0113] Step 601 of method 600 is similar to step 501 of method 500 (FIG. 5) described herein and includes receiving physiological data associated with a user. The physiological data can be received, for example, by electronic interface 119 (FIG. 1) described herein. This physiological data can be generated or obtained by at least one of one or more sensors 130 (FIG. 1). Information describing the physiological data received during the execution of step 601 can be stored in memory device 114 (FIG. 1). Step 601 differs from step 501 of method 500 (FIG. 5) in that step 601 includes receiving physiological data before, during, and / or after the first activity described at step 603 and is not limited to only before the user performs the first activity.

[0114] Step 602 of method 600 is identical or similar to step 502 of method 500 (FIG. 5) described herein and includes determining (step 501) the user's initial drowsiness level based at least in part on the received physiological data. For example, control system 110 can analyze the physiological data (stored, for example, in memory device 114) to determine the user's initial drowsiness level. Information describing the initial drowsiness level can be stored, for example, in memory device 114 (FIG. 1).

[0115] Step 603 of method 600 is the same as or similar to step 503 of method 500 (FIG. 5) described herein, and includes prompting the user to perform a first activity. In some implementations, for example, the user can be prompted via the user device 170 (e.g., via text, audio, or both). In such implementations, the control system 110 can cause the user device 170 to prompt the user to perform the first activity. The first activity can be, for example, a mental activity, a physical activity, reading, a game, a puzzle, viewing media content (e.g., video, movie, TV program, etc.), enjoying audio content (e.g., audiobook), exercise, movement (e.g., movement that consumes energy, movement that induces fatigue, etc.), yoga, meditation, breathing exercise, consumption of a beverage (e.g., warm milk), or any combination thereof. In some implementations, the user device 170 also provides or facilitates the first activity (e.g., via the display 172). The type / nature of the first activity can be selected, for example, based on the user's initial drowsiness and the likelihood that the first activity will change (e.g., increase) the user's drowsiness.

[0116] Step 604 of method 600 includes determining the user's second drowsiness level at least partially based on physiological data during a first portion of the first activity (step 601). Step 604 is similar to step 504 of method 500 (FIG. 5) described herein in that the second drowsiness level is determined after the user starts the first activity, but is different in that the second drowsiness level corresponds only to the first portion of the first activity. The first portion of the first activity can be, for example, at least about 10% of the total time of the first activity, at least about 25% of the total time of the first activity, at least about 33% of the total time of the first activity, at least about 50% of the total time of the first activity, at least about 75% of the total time of the first activity, and so on. In some implementations, step 604 further includes determining the rate of change between the initial drowsiness level (step 602) and the second drowsiness level (step 604).

[0117] In some implementations, step 604 also includes determining one or more respiratory parameters associated with the user during a first portion of the first activity, based at least in part on the physiological data (step 601). For example, the control system 110 can analyze the physiological data (stored, for example, in the memory device 114) to determine the respiratory parameters. The one or more respiratory parameters can include, for example, respiratory rate, respiratory amplitude, tidal volume change, inspiratory amplitude, expiratory amplitude, inspiratory to expiratory ratio, or any combination thereof.

[0118] Step 605 of method 600 includes modifying one or more parameters of the first activity in a second portion of the first activity, based at least in part on the determined second drowsiness level (step 604) and / or the first drowsiness level (step 602). The parameters of the first activity can include, for example, complexity, speed, volume, rhythm, tempo, music tempo, brightness, screen brightness, font size, font type, color, or any combination thereof. The parameter(s) of the first activity can generally be modified to enhance the effectiveness of the first activity in changing (e.g., enhancing) the user's drowsiness, as described herein. For example, when the first activity is displayed via the display device 172 of the user device 170, the screen brightness can be decreased and / or the color can be changed (e.g., changed to a warmer color, removing blue light, etc.) to reduce the load on the user's eyes and assist in enhancing the user's drowsiness.

[0119] As described above, in some implementations, step 604 also includes determining one or more respiratory parameters associated with. In such an implementation, step 605 may include changing the rhythm, tempo, music tempo, or any combination thereof of the second part of the first activity based at least on the one or more determined respiratory parameters to assist in reducing the user's respiratory rate. In some cases, the user's heart rate and / or respiratory rate can be substantially synchronized with the music the user is listening to. Therefore, for example, if the first activity is audio content (e.g., music), the rhythm, tempo, and / or music tempo can be changed (e.g., slowed down) to lower the user's respiratory rate and / or heart rate to assist in increasing the user's drowsiness.

[0120] As described above, in some implementations, step 604 further includes determining the rate of change between the first drowsiness level (step 602) and the second drowsiness level (step 604). In such an implementation, step 605 may include continuously changing one or more parameters of the first activity in the second part of the first activity based at least in part on the determined rate of change between the first drowsiness level and the second drowsiness level.

[0121] Referring to FIG. 7, a method 700 for determining one or more recommended activities to assist in changing a user's drowsiness level is shown. One or more steps of method 700 can be implemented using any element or aspect of system 100 (FIGS. 1-2) described herein.

[0122] Step 701 of method 700 includes receiving first physiological data and second physiological data associated with a user from one or more sensors. The first physiological data and the second physiological data can be received, for example, by the electronic interface 119 (FIG. 1) described herein. The first physiological data and the second physiological data can be generated or acquired by at least one of the one or more sensors 130 (FIG. 1). For example, the first physiological data can be generated by a first sensor or a first group of sensors among the sensors 130 described herein, and the second physiological data can be generated by a second sensor or a second group of sensors different from the first sensor or the first group of sensors. The first physiological data and the second physiological data can be generated and / or received at different times. For example, the first physiological data can be generated and received before the second physiological data is generated and / or received. Information describing the first physiological data and the second physiological data received in step 701 can be stored in the memory device 114 (FIG. 1).

[0123] Step 702 of method 700 also includes accumulating historical drowsiness data of the user, including a set of past record changes in the user's drowsiness level, in a user profile associated with the user, wherein each of the changes in the drowsiness level in the set of past record changes is associated with a corresponding one of a plurality of activities, and the historical drowsiness data is at least partially based on the first physiological data.

[0124] A user profile may include, for example, demographic information associated with the user, biometric information associated with the user, medical information associated with the user, user feedback by self-report, sleep parameters associated with the user (e.g., sleep-related parameters recorded from one or more previous sleep sessions), or any combination thereof. Demographic information may include, for example, information indicating the user's age, gender, race, family history of insomnia, employment status, educational status, socioeconomic status, or any combination thereof. Medical information may include, for example, one or more medical conditions associated with the user, drug use by the user, or both. Medical information data may further include the results or scores of a Multiple Sleep Latency Test (MSLT) and / or the scores or values of a Pittsburgh Sleep Quality Index (PSQI). User feedback by self-report may include information indicating a subjective sleep score (poor, average, good, etc.) by self-report, the user's subjective stress level by self-report, the user's subjective fatigue level by self-report, the user's subjective health status by self-report, recent life events experienced by the user, or any combination thereof.

[0125] Step 703 of method 700 is the same as or similar to step 502 of method 500 (FIG. 5) described herein, and includes determining the user's initial drowsiness level (step 701) based at least in part on the second physiological data.

[0126] Step 704 of method 700 includes training an algorithm using a user profile such that the algorithm is configured to (i) receive the user's initial drowsiness level as input information (step 703) and (ii) determine one or more recommended activities from a plurality of activities and output them to assist in changing the user's drowsiness level relative to the initial drowsiness level. This algorithm can be, for example, a machine learning algorithm (e.g., supervised or unsupervised) or a neural network (e.g., shallow or deep approach). This algorithm can be trained using data from the user profile data (step 702) and / or other data sources (e.g., data associated with individuals other than the user).

[0127] In some implementations, method 700 also includes communicating information indicating one or more recommended activities to the user (e.g., using user device 170 (FIG. 1) described herein). In other implementations, method 700 may include, subsequent to step 704, automatically initiating or starting one or more recommended activities on user device 170.

[0128] Referring to FIG. 8, a method 800 for calibrating a sensor for determining a user's drowsiness level is shown. One or more steps of method 800 can be implemented using any element or aspect of system 100 (FIGS. 1-2) described herein.

[0129] Step 801 of method 800 includes generating first physiological data associated with a user during a first period using a first sensor. In some implementations, the first sensor is the EEG sensor 158 (FIG. 1) described herein. In some implementations, the first sensor is the PPG 154 (FIG. 1) described herein. The first physiological data can be received, for example, by the electronic interface 119 (FIG. 1) described herein. Information describing the first physiological data received during the execution of step 801 can be stored in the memory device 114 (FIG. 1).

[0130] Step 802 of method 800 includes generating second physiological data associated with a user during a first period using a secondary sensor. The secondary sensor is different from the first sensor used during the execution of step 801. In some implementations, the secondary sensor is one (or a combination thereof) of the sensors 130 and is coupled or integrated with the user device 170 and / or the respiratory therapy system 120 (FIG. 1). The second physiological data can be received, for example, by the electronic interface 119 (FIG. 1) described herein. Information describing the second physiological data received during the execution of step 802 can be stored in the memory device 114 (FIG. 1).

[0131] Step 803 of method 800 includes determining a first sleepiness level of the user based on the first physiological data generated by the first sensor. The first sleepiness level can be determined, for example, by the control system 110 (FIG. 1) described herein.

[0132] Step 804 of method 800 includes determining a second sleepiness level of the user based on the second physiological data generated by the secondary sensor. The second sleepiness level can be determined, for example, by the control system 110 (FIG. 1) described herein.

[0133] Step 805 of method 800 includes calibrating the secondary sensor such that the determined second drowsiness level matches the determined first drowsiness level. As noted above, in some implementations, the first sensor can be the EEG sensor 158 (FIG. 1) described herein. The EEG sensor 158 often includes one or more electrodes disposed on the user's head to monitor electrical activity within the brain. Thus, the physiological data from the EEG sensor 158 can be used to determine the user's exact drowsiness level based on the user's brain activity. However, the EEG sensor 158 is not suitable for daily personal use because the electrodes are placed and it is difficult to handle.

[0134] In contrast, the secondary sensor generates various physiological data of the user from which the user's drowsiness level can be determined. However, since the secondary sensor does not directly measure brain activity, the determined drowsiness level may not be as accurate as when using the EEG sensor 158. Therefore, step 805 includes calibrating the secondary sensor such that the drowsiness level determined based on the data from the secondary sensor is approximately equal to (e.g., within 90% accuracy, within 92% accuracy, within 95% accuracy, etc.) the drowsiness level determined based on the data from the first sensor.

[0135] Referring to FIG. 9, a method 900 for recommending media content to a user based on the user's current drowsiness level is shown. One or more steps of method 900 can be implemented using any element or aspect of the system 100 (FIGS. 1 - 2) described herein.

[0136] Step 901 of method 900 includes displaying media content to a user. The media content can be displayed, for example, using the display device 172 of user device 170 (FIG. 1) described herein. The media content can include, for example, video content, audio content, television programs, movies, documentaries, video game sessions, document review sessions, programs, or any combination thereof. The media content can generally include a plurality of media content segments. For example, the first media content segment can be the first episode of a television program, and the second media content segment can be the second episode of the television program.

[0137] Step 902 of method 900 includes receiving physiological data associated with the user while the user is viewing the displayed media content. The physiological data can be received, for example, by the electronic interface 119 (FIG. 1) described herein. This physiological data can be generated or acquired by at least one of the one or more sensors 130 (FIG. 1). This physiological data can be generated, for example, by the sensors or sensor groups of sensors 130 described herein that are coupled or integrated with user device 170. Information describing the physiological data received during the execution of step 901 can be stored in the memory device 114 (FIG. 1).

[0138] Step 903 of method 900 includes generating a score for one or more of media content or a plurality of media content segments, at least in part based on the generated data associated with the user. For example, control system 110 can analyze physiological data (e.g., stored in memory device 114) to determine a score for media content. This score generally indicates the effectiveness of the media content in changing (e.g., increasing) the user's drowsiness level, similar to the activity score described herein (e.g., in step 506 of method 500). For example, if the media content is a fast-paced action movie, the media content can increase the user's alertness or attention and reduce the user's drowsiness level. In contrast, in another example, if the media content is something that is likely to be less engaging to the user (e.g., a TV show the user has seen before, a documentary, etc.), the media content can increase the user's drowsiness level. The determined score indicates the ability of the media content to change the drowsiness level of that particular user. Information describing the score(s) for media content or media content segments can be stored, for example, in memory device 114 (FIG. 1).

[0139] Step 904 of method 900 includes determining the user's current drowsiness level, at least in part based on the user's current physiological data. Step 904 is the same as or similar to step 502 of method 500 (FIG. 5) described herein, for example.

[0140] Step 905 of method 900 includes recommending media content to the user based at least in part on the user's current drowsiness level to assist in changing the user's current drowsiness level. For example, step 905 may include recommending one of a plurality of media content segments associated with a score as described above in step 903. Thus, step 905 may include recommending media content having a related score indicating that the media content is more effective in reducing the user's drowsiness level when the initial drowsiness level is low (e.g., the user is not close to falling asleep). The recommended media content can be determined, for example, by the control system 110 (FIG. 1).

[0141] In some implementations, step 905 includes recommending a plurality of consecutive media content segments. For example, step 905 may include recommending a first media content segment (e.g., a TV program episode) having a first score and then recommending a second media content segment (e.g., a different TV program episode) having a second score. These two segments can be sequentially presented to the user such that the effect of the two segments gradually increases the user's drowsiness level relative to the initial drowsiness level.

[0142] In some implementations, method 900 includes communicating to the user information indicating the recommendation (step 905). This recommendation can be communicated to the user, for example, via the user device 170 (e.g., via alphanumeric text or voice). Alternatively, method 900 may include automatically displaying the recommended media content to the user (e.g., the control system 110 causes the user device 170 to automatically display the recommended media content for the user to view).

[0143] One or more elements or aspects or steps or portions (singular or plural) from any one or more of the following claims 1 to 103 can be combined with one or more elements or aspects or steps or portions (singular or plural) from any one or more or a combination thereof of the other claims 1 to 103 to form one or more further implementations and / or claims of the present disclosure.

[0144] Although the present disclosure has been described with reference to one or more specific embodiments or implementations, those skilled in the art will recognize that numerous changes can be made without departing from the intent and scope of the present disclosure. These implementations and their obvious variations are each intended to fall within the intent and scope of the present disclosure. And it is also contemplated that further implementations according to various aspects of the present disclosure can combine any number of features from any of the implementations described herein.

Claims

1. a memory storing machine-readable instructions, first physiological data associated with a user, for receiving the first physiological data generated by a sensor, for determining a first drowsiness level of the user based at least in part on the first physiological data, for prompting the user via an electronic device to perform a first activity, subsequent physiological data associated with the user, for receiving subsequent physiological data generated by the sensor while the user is performing the first activity, after the user has performed the activity, or both, for determining a subsequent drowsiness level of the user based at least in part on the subsequent physiological data, and for determining a first activity score indicative of the effectiveness of the first activity in changing the user's drowsiness based at least in part on a difference between the first drowsiness level and the subsequent drowsiness level a control system including one or more processors configured to execute the machine-readable instructions. A system comprising the same.

2. The system according to claim 1, wherein the control system is further configured to determine whether the subsequent drowsiness level exceeds a predetermined threshold.

3. The system according to claim 2, wherein the control system is further configured to determine the first activity score based at least in part on a difference between a first time associated with the first drowsiness level and a second time associated with the subsequent drowsiness level that exceeds the predetermined threshold.

4. The system according to any one of claims 1 to 3, wherein the control system is further configured to prompt the user via the electronic device to perform a second activity based at least in part on the first activity score.

5. The system according to claim 4, wherein the complexity of the second activity is lower than the complexity of the first activity.

6. The system according to claim 4 or claim 5, wherein the duration of the second activity is longer than the duration of the first activity.

7. The system according to any one of claims 4 to 6, wherein the first activity, the second activity, or both include mental activities, physical activities, games, puzzles, reading, watching videos, enjoying audio content, exercise, movement, breathing exercises, or any combination thereof.

8. The control system receives information indicating the user's reaction to the provided stimulus, and determines the first activity score based at least in part on the information indicating the user's reaction to the provided stimulus. The system according to any one of claims 1 to 7, further configured as described above.

9. The system according to claim 8, wherein the information indicating the user's reaction includes reaction time, reaction accuracy, or both.

10. The system according to any one of claims 1 to 9, wherein the sensor includes an EEG sensor, an acoustic sensor, a camera, a motion sensor, an infrared sensor, or any combination thereof.

11. The system according to any one of claims 1 to 10, wherein the first physiological data, the subsequent physiological data, or both indicate the user's eye opening, the user's eye closing, the user's eye blinking, the user's pupil dilation, the user's body temperature, the user's head movement, the user's heart rate, the user's heart rate variability, the user's respiratory rate, or any combination thereof.

12. A memory storing machine-readable instructions, a first physiological data associated with the user, for receiving the first physiological data generated by the sensor, for determining the user's initial drowsiness level based at least in part on the first physiological data, for prompting the user to perform a first activity via an electronic device, for receiving a second physiological data associated with the user, wherein the subsequent physiological data is generated by the sensor while the user is performing the first activity, after the user has performed the first activity, or both, for determining the user's subsequent drowsiness level based at least in part on the second physiological data, and for determining a first activity score indicating the effectiveness of the first activity in changing the user's drowsiness based at least in part on the difference between the initial drowsiness level and the subsequent drowsiness level. A system comprising a control system including one or more processors configured to execute the machine-readable instructions.

13. The system according to claim 12, wherein the control system is further configured to receive third physiological data associated with the user in a sleep session after the user performs the first activity.

14. The system according to claim 13, wherein the control system is further configured to determine one or more sleep-related parameters associated with the user in the sleep session based at least in part on the third physiological data.

15. The system according to claim 14, wherein the one or more sleep-related parameters include a sleep latency parameter.

16. The system according to claim 14 or claim 15, wherein the control system is further configured to confirm the first activity score based at least in part on the one or more sleep-related parameters.

17. A memory storing machine-readable instructions, first physiological data associated with a user, for receiving first physiological data generated by a sensor, for determining a first sleepiness level of the user based at least in part on the first physiological data, for prompting the user to perform a first activity via an electronic device, subsequent physiological data associated with the user, for receiving subsequent physiological data generated by the sensor while the user is performing the first activity, after the user has performed the activity, or both, for determining a subsequent sleepiness level of the user based at least in part on the subsequent physiological data, for determining whether the subsequent sleepiness level exceeds a predetermined threshold, and for determining a first activity score indicating the effectiveness of the first activity in the change of the user's sleepiness based at least in part on a difference between a first time associated with the first sleepiness level and a second time associated with the subsequent sleepiness level that exceeds the predetermined threshold A system comprising a control system including one or more processors configured to execute the machine-readable instructions.

18. A memory storing machine-readable instructions, The first physiological data associated with a user, for receiving the first physiological data generated by a sensor, For determining a first drowsiness level of the user based at least in part on the first physiological data, To prompt the user via an electronic device to perform a first activity, Subsequent physiological data associated with the user, for receiving subsequent physiological data generated by the sensor while the user is performing the first activity, after the user has performed the activity, or both, For determining a subsequent drowsiness level of the user based at least in part on the subsequent physiological data, For determining a first activity score indicating the effectiveness of the first activity in the change of the user's drowsiness, based at least in part on a difference between the first drowsiness level and the subsequent drowsiness level, and To prompt the user via the electronic device to perform a second activity based at least in part on the first activity score Including one or more processors configured to execute the machine-readable instructions, The complexity of the second activity is lower than the complexity of the first activity, and / or the duration of the second activity is longer than the duration of the first activity A control system, and a system comprising the same.

19. A memory storing machine-readable instructions, The first physiological data associated with a user, for receiving the first physiological data generated by a sensor, For determining a first drowsiness level of the user based at least in part on the first physiological data, To prompt the user via an electronic device to perform a first activity, Subsequent physiological data associated with the user, for receiving subsequent physiological data generated by the sensor while the user is performing the first activity, after the user has performed the activity, or both, For determining a subsequent drowsiness level of the user based at least in part on the subsequent physiological data, For receiving information indicating the user's reaction to a provided stimulus, and To determine a first activity score indicating the effectiveness of the first activity in changing the user's drowsiness, based at least in part on the difference between the initial drowsiness level and the subsequent drowsiness level and information indicating the reaction A system comprising a control system including one or more processors configured to execute the machine-readable instructions. **Claim 20** A memory storing machine-readable instructions, First physiological data associated with a user, for receiving first physiological data generated by a sensor, To determine an initial drowsiness level of the user based at least in part on the first physiological data, To prompt the user to perform a first activity via an electronic device, Second physiological data associated with the user, for receiving second physiological data generated by the sensor while the user is performing the first activity, after the user has performed the activity, or both, To determine a subsequent drowsiness level of the user based at least in part on the second physiological data, To determine a first activity score indicating the effectiveness of the first activity in changing the user's drowsiness, based at least in part on the difference between the initial drowsiness level and the subsequent drowsiness level, and To receive third physiological data associated with the user in a sleep session after the user has performed the first activity, and to determine one or more sleep-related parameters associated with the user in the sleep session based at least in part on the third physiological data A system comprising a control system including one or more processors configured to execute the machine-readable instructions.

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