Abstract: The present disclosure is directed towards systems and methods for improving analysis of sleep data by classifying users based on sleeper type. In particular, a wearable computing system can obtain a first set of motion sensor data from the motion sensor for a user during a first period. The wearable computing system can determine a sleeper type from a plurality of sleeper types for the user based on the first set of motion sensor data received from the motion sensor. The wearable computing system can select a sleep analysis model from a plurality of sleep analysis models based on the sleeper type determined for the user. The wearable computing system can use the selected sleep analysis model to analyze a second set of motion sensor data from a second period to determine one or more sleep characteristics for the user during the second period.

Abstract: Physiological variables, metrics, biomarkers, and other data points can be used, in connection with a non-invasive wearable device, to screen for, and predict, mental health issues and cognitive states. In addition to metrics such as heart rate, sleep data, activity level, gamification data, and the like, information such as text message and email data, as well as vocal data obtained through a phone and/or a microphone, may be analyzed, provided user authorization. Applying predictive modeling, one or more of the monitored metrics can be correlated with mental states and disorders. Identified patterns can be used to update the predictive models, such as via machine learning-trained models, as well as to update individual event predictions. Information about the mental state predictions, and updates thereto, can be surfaced to the user accordingly.

Abstract: Assessing the sleep quality of a user in association with an electronic device with one or more physiological sensors includes detecting an attempt by the user to fall asleep, and collecting physiological information associated with the user. The disclosed method of assessing sleep quality may include determining respective values for one or more sleep quality metrics, including a first set of sleep quality metrics associated with sleep quality of a plurality of users, and a second set of sleep quality metrics associated with historical sleep quality of the user, based at least in part on the collected physiological information and at least one wakeful resting heart rate of the user, and determining a unified score for sleep quality of the user, based at least in part on the respective values of the one or more sleep quality metrics.

Abstract: Arousal events can be determined for a user associated with a wearable device, such as a user wearing a wearable computing device including one or more sensors. The one or more sensors may obtain EDA information that may determine a sympathetic nervous system response of the user, which may be responsive to an arousal event or an activation. Detection of events that increase the EDA response may provide information to the user regarding arousal events and provide recommendations to the user to address the arousal events to decrease their response.

Abstract: Arousal events can be determined for a user associated with a wearable device, such as a user wearing a wearable computing device including one or more sensors. The one or more sensors may obtain EDA information that may determine a sympathetic nervous system response of the user, which may be responsive to an arousal event or an activation. Detection of events that increase the EDA response may provide information to the user regarding arousal events and provide recommendations to the user to address the arousal events to decrease their response.

Abstract: Various types of data can be collected regarding the physical or mental health of a user, as may relate to sleep of the user over a period of time. Health metrics can be determined from this data that can enable the user to be associated with a particular health type or category. For sleep, this can include associating the user with a sleep animal that has specific characteristics. This can help a user to better understand that user's sleep, and how that sleep compares to sleep of others. In addition to being able to provide health information in a way that is easy to understand, such an approach can also help to make more accurate recommendations or take specific actions to help improve the health of a user, such as to improve sleep. This can include making recommendations to a user or automatically adjusting operation of at least one device.

Abstract: Various types of data can be collected regarding the physical or mental health of a user, as may relate to sleep of the user over a period of time. Health metrics can be determined from this data that can enable the user to be associated with a particular health type or category. For sleep, this can include associating the user with a sleep animal that has specific characteristics. This can help a user to better understand that user's sleep, and how that sleep compares to sleep of others. In addition to being able to provide health information in a way that is easy to understand, such an approach can also help to make more accurate recommendations or take specific actions to help improve the health of a user, such as to improve sleep. This can include making recommendations to a user or automatically adjusting operation of at least one device.

Abstract: Various types of data can be collected regarding the physical or mental health of a user, as may relate to sleep of the user over a period of time. Health metrics can be determined from this data that can enable the user to be associated with a particular health type or category. For sleep, this can include associating the user with a sleep animal that has specific characteristics. This can help a user to better understand that user's sleep, and how that sleep compares to sleep of others. In addition to being able to provide health information in a way that is easy to understand, such an approach can also help to make more accurate recommendations or take specific actions to help improve the health of a user, such as to improve sleep. This can include making recommendations to a user or automatically adjusting operation of at least one device.

Abstract: Assessing the sleep quality of a user in association with an electronic device with one or more physiological sensors includes detecting an attempt by the user to fall asleep, and collecting physiological information associated with the user. The disclosed method of assessing sleep quality may include determining respective values for one or more sleep quality metrics, including a first set of sleep quality metrics associated with sleep quality of a plurality of users, and a second set of sleep quality metrics associated with historical sleep quality of the user, based at least in part on the collected physiological information and at least one wakeful resting heart rate of the user, and determining a unified score for sleep quality of the user, based at least in part on the respective values of the one or more sleep quality metrics.

Abstract: Arousal events can be determined for a user associated with a wearable device, such as a user wearing a wearable computing device including one or more sensors. The one or more sensors may obtain EDA information that may determine a sympathetic nervous system response of the user, which may be responsive to an arousal event or an activation. Detection of events that increase the EDA response may provide information to the user regarding arousal events and provide recommendations to the user to address the arousal events to decrease their response.

Abstract: Arousal events can be determined for a user associated with a wearable device, such as a user wearing a wearable computing device including one or more sensors. The one or more sensors may obtain EDA information that may determine a sympathetic nervous system response of the user, which may be responsive to an arousal event or an activation. Detection of events that increase the EDA response may provide information to the user regarding arousal events and provide recommendations to the user to address the arousal events to decrease their response.

Abstract: Physiological variables, metrics, biomarkers, and other data points can be used, in connection with a non-invasive wearable device, to screen for, and predict, mental health issues and cognitive states. In addition to metrics such as heart rate, sleep data, activity level, gamification data, and the like, information such as text message and email data, as well as vocal data obtained through a phone and/or a microphone, may be analyzed, provided user authorization. Applying predictive modeling, one or more of the monitored metrics can be correlated with mental states and disorders. Identified patterns can be used to update the predictive models, such as via machine learning-trained models, as well as to update individual event predictions. Information about the mental state predictions, and updates thereto, can be surfaced to the user accordingly.

Abstract: Assessing the sleep quality of a user in association with an electronic device with one or more physiological sensors includes detecting an attempt by the user to fall asleep, and collecting physiological information associated with the user. The disclosed method of assessing sleep quality may include determining respective values for one or more sleep quality metrics, including a first set of sleep quality metrics associated with sleep quality of a plurality of users, and a second set of sleep quality metrics associated with historical sleep quality of the user, based at least in part on the collected physiological information and at least one wakeful resting heart rate of the user, and determining a unified score for sleep quality of the user, based at least in part on the respective values of the one or more sleep quality metrics.

Abstract: Assessing the sleep quality of a user in association with an electronic device with one or more physiological sensors includes detecting an attempt by the user to fall asleep, and collecting physiological information associated with the user. The disclosed method of assessing sleep quality may include determining respective values for one or more sleep quality metrics, including a first set of sleep quality metrics associated with sleep quality of a plurality of users, and a second set of sleep quality metrics associated with historical sleep quality of the user, based at least in part on the collected physiological information and at least one wakeful resting heart rate of the user, and determining a unified score for sleep quality of the user, based at least in part on the respective values of the one or more sleep quality metrics.

Abstract: Assessing the sleep quality of a user in association with an electronic device with one or more physiological sensors includes detecting an attempt by the user to fall asleep, and collecting physiological information associated with the user. The disclosed method of assessing sleep quality may include determining respective values for one or more sleep quality metrics, including a first set of sleep quality metrics associated with sleep quality of a plurality of users, and a second set of sleep quality metrics associated with historical sleep quality of the user, based at least in part on the collected physiological information and at least one wakeful resting heart rate of the user, and determining a unified score for sleep quality of the user, based at least in part on the respective values of the one or more sleep quality metrics.

Abstract: Assessing the sleep quality of a user in association with an electronic device with one or more physiological sensors includes detecting an attempt by the user to fall asleep, and collecting physiological information associated with the user. The disclosed method of assessing sleep quality may include determining respective values for one or more sleep quality metrics, including a first set of sleep quality metrics associated with sleep quality of a plurality of users, and a second set of sleep quality metrics associated with historical sleep quality of the user, based at least in part on the collected physiological information and at least one wakeful resting heart rate of the user, and determining a unified score for sleep quality of the user, based at least in part on the respective values of the one or more sleep quality metrics.

Abstract: Assessing the sleep quality of a user in association with an electronic device with one or more physiological sensors includes detecting an attempt by the user to fall asleep, and collecting physiological information associated with the user. The disclosed method of assessing sleep quality may include determining respective values for one or more sleep quality metrics, including a first set of sleep quality metrics associated with sleep quality of a plurality of users, and a second set of sleep quality metrics associated with historical sleep quality of the user, based at least in part on the collected physiological information and at least one wakeful resting heart rate of the user, and determining a unified score for sleep quality of the user, based at least in part on the respective values of the one or more sleep quality metrics.

Abstract: Assessing the sleep quality of a user in association with an electronic device with one or more physiological sensors includes detecting an attempt by the user to fall asleep, and collecting physiological information associated with the user. The disclosed method of assessing sleep quality may include determining respective values for one or more sleep quality metrics, including a first set of sleep quality metrics associated with sleep quality of a plurality of users, and a second set of sleep quality metrics associated with historical sleep quality of the user, based at least in part on the collected physiological information and at least one wakeful resting heart rate of the user, and determining a unified score for sleep quality of the user, based at least in part on the respective values of the one or more sleep quality metrics.