Abstract: 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.

Abstract: 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 physiological attributes of the user. In particular, the wearable strap may be used to monitor heart rate data including a resting heart rate, a heart rate variability, and sleep quality—e.g., to generate a recovery indicator that provides a quantitative assessment of a physical recovery state of the user indicating whether the user is recovered and ready for exercise.

Abstract: A wearable physiological measurement system may include, inter alia, sensors and circuitry for automatically and continually determining a heart rate of a wearer. The system can be charged while worn by the wearer via coupling with a removable modular housing, which may itself provide additional functionality such as a multi-function watch.

Abstract: 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.

Abstract: Heart rate data from a wearable physiological monitor can be used to determine a respiratory rate for a wearer. Using this respiratory rate data, a respiratory rate baseline for a wearer can be determined and used to detect variations from the baseline that indicate onset of conditions such as Covid-19 or other respiratory infections and the like.

Abstract: A controllable window of time is provided for waking a user from sleep. A system uses this window to variably control the acquisition of physiological data from a device such as a wearable monitor, such as by initiating data acquisition at the beginning of the window, and the acquired data can be used in turn to control when, during the window, an active alarm to the user might be provided. Using this technique, data acquisition from a physiological monitoring device or the like can be increased around the onset of the window to more accurately calculate a suitable waking time for the user within the window. This advantageously avoids the need for continuous, high-frequency data communications during long intervals of sleep, and focuses data transmission, related communications, and computing resources on those intervals when up-to-date data might be most useful for optimizing the user's wake up experience.

Abstract: Embodiments provide physiological measurement systems, devices and methods for continuous health and fitness monitoring. A lightweight wearable system is provided to collect various physiological data continuously from a wearer without the need for a chest strap. The system also enables monitoring of one or more physiological parameters in addition to heart rate including, but not limited to, body temperature, heart rate variability, motion, sleep, stress, fitness level, recovery level, effect of a workout routine on health, caloric expenditure. Embodiments also include computer-executable instructions that, when executed, enable automatic interpretation of one or more physiological parameters to assess the cardiovascular intensity experienced by a user (embodied in an intensity score or indicator) and the user's recovery after physical exertion (embodied in a recovery score). These indicators or scores may be displayed to assist a user in managing the user's health and exercise regimen.

Abstract: 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.

Abstract: 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.

Abstract: A garment provides infrastructure for using one or more physiological monitoring devices. This may include connecting infrastructure such as physical restrains to securely retain monitoring devices during use, supporting infrastructure such as an intra-garment communications bus, external communications infrastructure, power sources, and the like, as well as augmentation infrastructure to augment monitors with, e.g., processing power, geolocation services, user interfaces, additional sensors, and so forth.

Abstract: Embodiments provide physiological measurement systems, devices and methods for cardiovascular intensity measurement. In an aspect, a wearable device receives a time series of heart rate data for a user. The time series of heart rate data can be weighted and summed according to a weighting scheme. The weighted and summed time series of heart rate data can be used to generate an intensity score indicating the cardiovascular intensities experienced by the user during a desired period of time.

Abstract: 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.

Abstract: Variations in pulse shape over time can be used to draw inferences about activity, health, and age of an individual. For example, PPG pulses may be mapped to a latent space where variations in shape can be measured directly in terms of distance between pulses. In one aspect, pulse-to-pulse comparisons for an individual can be used to estimate strain, recovery, sleep, and so forth. Longer term measurements (e.g., over weeks, month, or years) can be used to detect changes in health and fitness for the individual. In another aspect, pulse-to-pulse comparisons among different individuals can be used to estimate relative cardiovascular health, age, and the like.

Abstract: 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.

Abstract: 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.

Abstract: Cycles of awakeness and sleep are flexibly but uniquely mapped to calendar days in order to facilitate user interactions with continuously monitored physiological data, and to facilitate meaningful quantitative assessments of sleep, recovery, and physical strain over user cycles that vary above and below twenty four hours in duration.

Abstract: 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.

Abstract: 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.

Abstract: 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.