Fitbit, Inc.

Alexander Statan

Allison Maya Russell

Zachary Todd Beattie

Emma Jane Quinn

Conor Joseph Heneghan

Paul Francis Stetson

Lindsey Michelle Sunden

Jonathan David Charlesworth

Belen Lafon

Man-Chi Liu

Derrick Steven Vickers

Elena Perez

James Horng-Kuang Lin

Alicia Yolanda Kokoszka

Karla Theresa Gleichauf

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.

Determining mental health and cognitive state through physiological and other non-invasively obtained data

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.

Sleep scoring based on physiological information

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.

Detection and response to arousal activations

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.

Cluster-based sleep analysis

Growing community of inventors

Oakland, CA, United States of America

Alexander Statan