Fitbit, Inc.

Logan Niehaus

Subramaniam Venkatraman

Shelten Gee Jao Yuen

Conor Joseph Heneghan

Hao-Wei Su

Jonathan David Charlesworth

Timothy Roberts

Chris Hanrahan Sarantos

Alexandros A Pantelopoulos

Jaclyn Leverett Wasson

Atiyeh Ghoreyshi

Belen Lafon

Thomas Samuel Elliot

Peter Colin Dess

Zachary Todd Beattie

Jonathan Wonwook Kim

Amir Bahador Farjadian

Allison Shih Wu

Peter Duyan

Eric M Foxlin

Andrew Larsen Axley

Veera Venkata Sesha Sai Vaddi

Johnathan Charlesworth

Timothy M Roberts

A wearable device for tracking swim activities of a user is provided. The wearable device may include one or more sensors configured to generate sensor data, and based on the sensor data, the wearable device may determine swim metrics such as swim stroke count, swim stroke type, swim lap count, and swim speed. The determined swim metrics may be filtered based on one or more swim periods during which the user is likely to have been swimming. The wearable device may determine such swim periods based on the sensor data and/or the determined swim metrics.

Automatic detection and quantification of swimming

Disclosed are devices and methods for estimating blood pressure, which implement a pulse-transit-time-based blood pressure model that can be calibrated. Some implementations provide reliable and user friendly means for calibrating the blood pressure model using blood pressure perturbation methods and multiple sensors.

Calibration of pulse-transit-time to blood pressure model using multiple physiological sensors and various methods for blood pressure variation

Approaches described herein can capture an audio signal using at least one microphone while a user of an electronic device is determined to be asleep. At least one audio frame can be determined from the audio signal. The at least one audio frame represents a spectrum of frequencies detected by the at least one microphone over some period of time. One or more sounds associated with the at least one audio frame can be determined. Sleep-related information can be generated. The information identifies the one or more sounds as potential sources of sleep disruption.

Detecting and measuring snoring

Approaches described herein can determine one or more breathing phase patterns over a period of time using audio data captured by at least one microphone. The audio data can include one or more snores. A breathing phase pattern included within the period of time can be determined based at least in part on sensor data captured by one or more sensors in the electronic device. A determination can be made that a first breathing phase pattern represented by the audio data and a second breathing phase pattern represented by the sensor data are correlated. A determination can be made that the first breathing phase pattern represented by the audio data and the second breathing phase pattern represented by the sensor data both correspond to a user wearing the electronic device.

Health information for a woman can be used to predict timing of events related to the woman's menstrual cycle. If available, historical cycle information for a woman can be used to predict upcoming cycle events, such as the start and stop of menstruation. To improve the accuracy of those predictions, one or more health metrics are monitored for the woman that can be correlated with the menstrual cycle. These can include, for example, the resting heart rate (RHR), blood oxygen concentration (SpO) level, and hemoglobin concentration, among other such options. The metrics are monitored over time to determine patterns that can be correlated with menstrual cycle. This information can then be used to update the predictive model, as well as to update individual event predictions. Information about the predictions, and updates to the predictions, can be surfaced accordingly.

Menstrual cycle tracking

This disclosure provides devices and methods for estimating blood pressure using intelligent oscillometric blood pressure measurement techniques, where some implementations of the devices include multiple biometric sensors and/or can obtain sensor data from a connected device. In some implementations, the devices automatically determine an identity of a user. In some implementations, the devices automatically provide instructions to users to take blood pressure measurements. In some implementations, the devices applied intelligent inflation techniques to improve user comfort and speed up measurements.

Intelligent inflatable cuff for arm-based blood pressure measurement

Techniques for automatic tracking of user data for exercises are disclosed. In one aspect, a method of operating a wearable device may involve determining, based on output of one or more biometric sensors, that a user of the wearable device has started an exercise and identifying a type of the exercise that the user has started based on comparing the output of the one or more biometric sensors to defined sensor data for a plurality of exercise types. The method may further include adjusting a global positioning system (GPS) receiver in response to determining the start of the exercise and based on the type of the exercise.

Automatic tracking of geolocation data for exercises

Techniques for automatic tracking of user data for exercises are disclosed. In one aspect, a method of operating a wearable device may involve determining that a user of the wearable device has started an exercise, activating the GPS receiver in response to determining that the user has started the exercise, and detecting a time at which the GPS receiver achieves an initial GPS fix of a location of the wearable device. The method may further involve logging, based on output of the one or more biometric sensors, a first set of user data during a first time interval between the start of the exercise and the detected time of the initial GPS fix, and back-filling an exercise route of the user during the first time interval based on the first set of user data.

Back-filling of geolocation-based exercise routes

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Alameda, CA, United States of America

Logan Niehaus