FITBIT, INC.

A wearable device is wearable on an appendage of a user and includes a force-sensitive display screen to generate force data when a force is applied to the force-sensitive display screen, a photoplethysmogram (PPG) sensor, and one or more processors. The PPG sensor is positioned below an external surface of the force-sensitive display screen and generates PPG sensor data when the force is applied to the force-sensitive display screen. The one or more processors are configured to: determine, from the PPG sensor data and the force data, a variation of PPG blood volume pulse amplitude with a variation in pressure applied to the appendage, and calculate an estimate of blood pressure of the appendage from the variation of PPG blood volume pulse amplitude with the variation in the pressure applied to the appendage.

The present disclosure provides computer-implemented methods, systems, and devices for mitigating noise in a signal. A user computing device comprises physiological signal sensor configured to generate a first signal relating one or more characteristics associated with a user. The user computing device further comprises a transducer configured to generate a noise signature signal based on detection of one or more noise sources. The user computing device further comprises one or more control circuits configured to modify the first signal based on the noise signature signal to generate a modified first signal. The control circuits are further configured to determine one or more characteristics associated with a user based on the modified first signal.

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 2 ) 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.

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.

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.

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.

A wearable computing device includes an electronic display with a configurable brightness level setting, a physiological metric sensor system including a light source configured to direct light into tissue of a user wearing the wearable computing device and a light detector configured to detect light from the light source that reflects back from the user. The device may further include control circuitry configured to activate the light source during a first period, generate a first light detector signal indicating a first amount of light detected by the light detector during the first period, deactivate the light source during a second period, generate a second light detector signal indicating a second amount of light detected by the light detector during the second period, generate a physiological metric based at least in part on the first light detector signal and the second light detector signal, and modify the configurable brightness level setting based on the second light detector signal.

Systems, devices, and methods for tracking one or more physiological metrics (e.g., heart rate, blood oxygen saturation, and the like) of a user are described. For example, one or more light sources and one or more light detectors may be positioned on a wearable device such that light can be emitted towards the user's skin and further such that light reflected back to the wearable device can be measured and used to generate values for the one or more physiological metrics.

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.

Disclosed are devices and methods for non-invasively measuring arterial stiffness using pulse wave analysis of photoplethysmogram data. In some implementations, wearable biometric monitoring devices provided herein for measuring arterial stiffness have the ability to automatically and intelligently obtain PPG data under suitable conditions while the user is engaged in activities or exercises. In some implementations, wearable biometric monitoring devices are provided herein with the ability to remove PPG data variance caused by factors unrelated to arterial stiffness. In some implementations, wearable biometric monitoring devices have the ability to perform PWA while accounting for the user’s activities, conditions, or status.