Exertion-driven physiological monitoring and prediction method and system

A method, comprising:

• receiving, from one or more exertion monitors, one or more exertion signals indicating one or more motion related metrics of a subject;
• receiving, from one or more physiological response monitors, one or more physiological signals, including a measured heart rate signal, of the subject;
• acquiring for the subject data values from the received one or more exertion signals and data values from the received one or more physiological signals, including the measured heart rate signal, over a first period of time during a first exertion event for the subject;
• creating and training, through machine learning by one or more processing circuits, one or more dynamic physiological response models for the subject from a combination of the data values from the received one or more exertion signals and data values from the received one or more physiological signals, including the measured heart rate signal, over the first period of time, the one or more dynamic physiological response models configured to synthetically generate a heart rate signal for the subject in real time from data values received from the exertion monitors in an absence of the one or more physiological signals, including the absence of the measured heart rate signal, indicating a heart rate value of the subject during a subsequent exertion event that corresponds to the subject's actual heart rate with a confidence level above a threshold value the training including:
• generating exertion protocol regimes to be executed by the subject on exercise equipment over the first period of time:
• while acquiring for the subject data values from the received one or more exertion signals and data values from the received one or more physiological signals over the first period of time, changing exercise operating parameters for the exercise equipment;
• acquiring data values from the received one or more exertion signals of the subject during a second period of time during a second exertion event for the subject, the second exertion event being subsequent to the first exertion event;
• and
• in response to the absence of the one or more physiological signals, including the absence of the measured heart rate signal, of the subject during the second time period, generating, by the one or more processing circuits during the second period of time from the data values from the received one or more exertion signals during the second period of time and from one or more of the dynamic physiological response models for the subject, the synthetically generated heart rate signal, including the heart rate value, of the subject in response to the second exertion event in real time during the second exertion event.