Whoop, Inc.

John Vincenzo Capodilupo

William Ahmed

Aurelian Nicolae

Brett Tobias Tom

Carl Johan Liden

Mostafa Ghannad-Rezaie

Emily Rachel Capodilupo

Behnoosh Tavakoli

Rinat Aruh

Brian Anthony Todd

Emily Rachel Breslow

Johan Liden

Benjamin August Rothenberg Peterson

Eliot Reshetar-Jost

Nick Burrows

Victoria Harrison Lee

Sleep need for a user is assessed using continuous physiological data from a wearable monitor. In particular, by calculating a first sleep debt metric based on user strain and a second sleep debt metric based on accumulated sleep debt, an objective metric can be obtained that estimates an amount of sleep needed by the user in a next sleep period. This approach takes advantage of multiple modes of information embedded in the physiological data, such as a sleep and exercise patterns for a user over one or more preceding days.

Determining sleep need from physiological measurements

A physiological monitor switches between a time domain analysis and a frequency domain analysis for determining a heart rate based on objective indicia of signal quality in an acquired heart rate signal.

Determining heart rate with reflected light data

A model of data quality is derived for physiological monitoring with a wearable device by comparing data from the wearable device to concurrent data acquisition from a ground truth device such as a chest strap or electrocardiography (EKG) heart rate monitor. With this comparative data, a machine learning model or the like may be derived to prospectively evaluate data quality based on the data acquisition context, as determined, for example, by other sensor data and signals from the wearable device.

Applied data quality metrics for physiological measurements

A wearable physiological monitoring device is configured to determine a wearer's sleep intention. In particular, the device evaluates activity that might be objectively associated with awaking from sleep in order to distinguish between a wearer's intention to end sleep and transitory stirring or other intermittent activity occurring in the context of a longer sleep interval. By evaluating sleep intention more accurately in this manner, the device can advantageously request sleep analysis from a remote server in those instances where a user intends to end a period of sleep (so that this data can be available to the user more quickly), while avoiding unnecessary or redundant server-side processing of sleep data in those instances where a user intends to continue sleeping.

Detecting sleep intention

Disclosed herein is a device for continuous physiological monitoring as well as systems and methods for interpreting data from such a device. The systems and methods may include automatically detecting, assessing, and analyzing exercise activity, physical recovery states, sleep states, and the like. The acquisition of continuous physiological data may facilitate automated recommendations concerning changes to sleep, recovery time, exercise routines, and the like.

Automated exercise recommendations

Embodiments provide physiological measurement systems, devices and methods for continuous health and fitness monitoring. A wearable strap may detect reflected light from a user's skin, where data corresponding to the reflected light is used to automatically and continually determine a heart rate of the user. The wearable strap may monitor heart rate data including heart rate variability, resting heart rate, and sleep quality. The systems may include a processing module that generates an indicator of physical recovery based on the heart rate data. The recovery indicator may be used to determine strain related to an exercise routine, qualitative information on the user's health, whether to alter a user's exercise plan, and so forth.

Determining a recovery indicator using heart rate data

Wearable physiological monitoring device

A wearable device supports continuous wearability and operation with a supplemental set of removable and replaceable batteries that recharge a first set of batteries powering the device. In an aspect, the wearable system includes a head portion coupled to an appendage of a user, where the head portion includes an electronic system powered by a first set of batteries, and a modular housing releasably engageable to the head portion that includes a second set of batteries. In this manner, the modular housing can be removed and recharged independent from the head portion, and then recoupled to the head portion to recharge the first set of batteries. Thus, in an aspect, the first set of batteries can continuously power the electronic system without a need for removal of the head portion. Such a system can be particularly advantageous for continuous, uninterrupted health and fitness monitoring.

Continuously wearable monitoring device

Disclosed herein is a device for continuous physiological monitoring as well as systems and methods for interpreting data from such a device. The device may support intelligent selection from among two or more different modes for heart rate detection. In addition, the acquisition of continuous physiological data facilitates automated recommendations concerning changes to sleep, recovery time, exercise routines and the like.

Continuous heart rate monitoring and interpretation

A variety of techniques are used automate the collection and classification of workout data gathered by a wearable physiological monitor. The classification process is staged in order to correctly and efficiently characterize a workout type. Initially, a generalized workout event is detected using motion and heart rate data. Then a location of the monitor on a user is determined. An artificial intelligence engine can then be conditionally applied (if a workout is occurring and a suitable device location is detected) to identify the type of workout. In addition to improved speed and accuracy, a workout detection process implemented in this manner can be realized with a sufficiently small computational footprint for deployment on a wearable physiological monitor.

Activity recognition

Embodiments provide physiological measurement systems, devices and methods for continuous health and fitness monitoring. A wearable strap may detect reflected light from a user's skin, where data corresponding to the reflected light is used to automatically and continually determine a heart rate of the user. The wearable strap may include a motion sensor that is used to determine a motion status of the user. Based upon the motion status of the user, the system may activate light emitters on the wearable strap to determine the heart rate of the user.

Physiological measurement system with motion sensing

Continuous monitoring of a plurality of physiological data may be used for health and fitness improvements for a user. To this end, a physiological monitoring and measurement device may include a wearable strap that receives heart rate data for a user including a time series of heart rate data, a maximum heart rate, and a resting heart rate. A processor may transform the heart rate data into a time series of heart rate reserve data that is weighted, e.g., to account for cardiovascular efficiencies at different intensity levels, to provide a weighted time series of heart rate reserve values. An intensity score that provides an indicator of cardiovascular intensity for an exercise routine may be generated from the weighted time series of heart rate reserve values and displayed to the user on the wearable strap.

Device for physiological monitoring and measurement

A system uses continuous tracking of sleep activity and heart rate activity to evaluate heart rate variability immediately before transitioning to an awake state, e.g., at the end of the last phase of deep sleep. In particular, a wearable, continuous physiological monitoring system as described herein includes one or more sensors to detect sleep states, the transitions between sleep states, and the transitions from a sleep state to an awake state for a user. This information can be used in conjunction with continuously monitored heart rate data to calculate heart rate variability of the user at the end of the last phase of sleep preceding the user waking up. By using the history of heart rate data in conjunction with sleep activity in this manner, an accurate and consistent recovery score can be calculated based on heart rate variability.

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Boston, MA, United States of America

John Vincenzo Capodilupo