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: Physiological metrics such as respiratory rate, resting heart rate, heart rate variability, temperature, and the like can be measured over time for a user and correlated to reproductive phases. By determining the chronological phase in a hormonal cycle or the like, automated recommendations for sleep, diet, exercise and the like can be provided in a phase-coordinated manner.

Abstract: According to a first aspect, there is provided a computer-implemented method for predicting a date of delivery, comprising acquiring heart rate data for a pregnant user from wearable sensor system, determining a heart rate variability and/or a resting heart rate based on the heart rate data, and predicting the date of delivery using the resting heart rate and/or the heart rate variability.

Abstract: A physiological monitor uses patterns of motion during strength training activity, e.g., as detected by a wearable monitor, to evaluate a degree of muscular, musculoskeletal, and/or biomechanical strain experienced by a user while engaged in strength training. The resulting strain may advantageously be quantified and used to provide coaching recommendations, update daily strain metrics, and take other responsive actions.

Abstract: A physiological monitor uses patterns of motion during strength training activity, e.g., as detected by a wearable monitor, to evaluate a degree of muscular, musculoskeletal, and/or biomechanical strain experienced by a user while engaged in strength training. The resulting strain may advantageously be quantified and used to provide coaching recommendations, update daily strain metrics, and take other responsive actions.

Abstract: Physiological parameters such as respiratory rate and/or heart rate variability can be measured over time for a user and correlated to physiological and/or hormonal cycles such as the menstrual cycle. By determining the phase of such a cycle in this manner, an automatic coach for the user can recommend phase-specific adjustments to activities such as sleep and exercise.

Abstract: Physiological parameters such as respiratory rate and/or heart rate variability can be measured over time for a user and correlated to physiological and/or hormonal cycles such as the menstrual cycle. By determining the phase of such a cycle in this manner, an automatic coach for the user can recommend phase-specific adjustments to activities such as sleep and exercise.

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: Physiological parameters such as respiratory rate and/or heart rate variability can be measured over time for a user and correlated to physiological and/or hormonal cycles such as the menstrual cycle. By determining the phase of such a cycle in this manner, an automatic coach for the user can recommend phase-specific adjustments to activities such as sleep and exercise.

Abstract: Physiological parameters such as respiratory rate and/or heart rate variability can be measured over time for a user and correlated to physiological and/or hormonal cycles such as the menstrual cycle. By determining the phase of such a cycle in this manner, an automatic coach for the user can recommend phase-specific adjustments to activities such as sleep and exercise.

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: 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: 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.

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.