Fitbit, Inc.

Suraj Gowda

Daniel Joel Freschl

Shelten Gee Jao Yuen

Peter W Richards

Conor Joseph Heneghan

Lindsey Michelle Sunden

Chris Hanrahan Sarantos

Anjian Wu

Peter Colin Dess

Aditya Vivekanand Nadkarni

Jonathan Wonwook Kim

Brett Adam Coakley

Tracy Norman Giest

Danel Joel Freschl

The accuracy of physiological data measured through contact with skin can be validated by characterizing the forces at the surfaces where data is measured. Conventional devices do not monitor the fit of skin-based sensors, making the accuracy and confidence in physiological data dependent on the user ensuring that the device is fitted properly. Over time, the seating of a device will vary due to changes in user activity and the need to periodically remove a device. Inevitably, instances will arise where the device is not fitted correctly, which may result in skewed physiological metrics. By monitoring the forces acting on the housing of a device, the interface of skin sensors can be characterized allowing for confidence metrics in the corresponding physiological data to be determined. In some cases, a user can be notified when a device is not seated properly, and in some cases, data may even be calibrated based on the fit of a device.

Pressure sensor integration into wearable device

Approaches to determining a sleep fitness score for a user are provided, such as may be based upon monitored breathing disturbances of a user. The system receives user state data generated over a time period by a combination of sensors provided via a wearable tracker associated with the user. A system can use this information to calculate a sleep fitness score, breathing disturbance score, or other such value. The system can classify every minute within the time period as either normal or atypical, for example, and may provide such information for presentation to the user.

Automated detection of breathing disturbances

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Berkeley, CA, United States of America

Suraj Gowda