Systems, devices and methods for continuous heart rate monitoring and interpretation

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.

1 - 20 . (canceled) 
     
     
         21 . A system for physiological monitoring and measurement comprising:
 a wearable physiological measurement device including one or more sensors configured to continuously monitor a heart rate of a user;   a strap attached to the wearable physiological measurement device and couplable to an appendage of the user;   a memory storing a machine learning model trained with heart rate data from a group of individuals to generate coefficient estimates for weighting a time series of heart rate data; and   one or more processors coupled in a communicating relationship with the wearable physiological measurement device, the one or more processors configured to:   receive heart rate data for the user from the wearable physiological measurement
 device, the heart rate data including the time series of heart rate data for the user; 
 generate a set of coefficient estimates for weighting the time series of heart rate data using the machine learning model; 
 weight the time series of heart rate data according to the set of coefficient estimates, thereby providing a weighted time series of heart rate data; 
 sum the weighted time series of heart rate data for a predetermined unit interval to provide a summed weighted time series of heart rate data; and 
 generate an intensity score for the user providing an indicator of cardiovascular intensity based on the summed weighted time series of heart rate data over the predetermined unit interval. 
   
     
     
         22 . The system of  claim 21 , further comprising adapting the intensity score for physiological properties of the user by training the machine learning model with data from the user. 
     
     
         23 . The system of  claim 21 , wherein the one or more processors are configured to weight the time series of heart rate data based at least in part on previous intensity scores for the user. 
     
     
         24 . The system of  claim 21 , wherein the one or more processors are configured to weight the time series of heart rate data using two or more models of physiological processes for different levels of cardiovascular exertion by the user. 
     
     
         25 . The system of  claim 21 , further comprising a display with a user interface for presenting information from the wearable physiological measurement device to the user. 
     
     
         26 . The system of  claim 25 , wherein the user interface is configured to display the intensity score. 
     
     
         27 . The system of  claim 25 , wherein the user interface is configured to display one or more of a sleep score and a recovery score. 
     
     
         28 . The system of  claim 21 , wherein the one or more processors include at least one processor coupled to the strap. 
     
     
         29 . The system of  claim 21 , wherein the one or more processors include at least one processor of a server coupled with the wearable physiological measurement device through a communication network. 
     
     
         30 . The system of  claim 21 , wherein the wearable physiological measurement device includes a photoplethysmography system. 
     
     
         31 . The system of  claim 30 , further comprising a battery for recharging, the battery removably and replaceably couplable to the wearable physiological measurement device to enable continuous monitoring of the heart rate of the user with the wearable physiological measurement device. 
     
     
         32 . A system for physiological monitoring and measurement comprising:
 a wearable physiological measurement device including one or more sensors configured to continuously monitor a heart rate of a user;   a strap attached to the wearable physiological measurement device and couplable to an appendage of the user;   a memory storing a machine learning algorithm trained to determine when the user is awake and asleep; and   one or more processors coupled in a communicating relationship with the wearable physiological measurement device, the one or more processors configured to:
 receive heart rate data for the user from the wearable physiological measurement device, the heart rate data including a time series of heart rate data for the user; and 
 apply the machine learning algorithm to identify one or more sleep periods for the user. 
   
     
     
         33 . The system of  claim 32 , wherein the machine learning algorithm is trained to determine when the user is awake and asleep using user-specific input including the heart rate of the user. 
     
     
         34 . The system of  claim 32 , wherein the one or more processors are configured to calculate a pattern-determined sleep need for the user based on the one or more sleep periods. 
     
     
         35 . The system of  claim 32 , wherein the one or more processors are configured to calculate a historically-determined sleep debt for the user based on the one or more sleep periods. 
     
     
         36 . The system of  claim 32 , wherein the one or more processors generate a sleep score assessing the user's sleep quality during the one or more sleep periods. 
     
     
         37 . The system of  claim 36 , wherein the sleep score measures a combination of sleep efficiency and sleep duration. 
     
     
         38 . The system of  claim 32 , further comprising a user interface configured to display the sleep score. 
     
     
         39 . The system of  claim 32 , wherein the one or more processors include at least one processor coupled to the strap. 
     
     
         40 . The system of  claim 32 , wherein the one or more processors include at least one processor of a server coupled in a communicating relationship with the wearable physiological measurement device through a communication network.