Exertion-driven physiological monitoring and prediction method and system
Automated systems and methods are presented for determining the physiological response of human or suitable animal subjects to physical exertion. The methods and systems can include monitoring sensors that capture the motion of the subject along with corresponding physiological data, and can track such motion for the duration of a period of physical exertion. The system is able to acquire an initial stream of physiological data from the subject during a range of physical exertion activities that are representative of the events intended to be monitored with the proposed method and system, enabling a corresponding dynamic physiological response model to be created. The motion tracking system and physiological response model can then be used to predict the physiological response to physical exertion events under a prescribed framework, including applications during real-time event monitoring.
1. 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.
2. The method of claim 1 , further comprising:
acquiring data values from the received one or more physiological signals during the second exertion event for the subject; and
updating, during the second period of time, the generating of the physiological response including the synthetically generated heart rate signal of the subject in response to the second exertion event based on the data values from the physiological signals during the second exertion event for the subject.
3. The method of claim 1 , wherein the first period of time and the second period of time at least partially overlap and the first exertion event and the second exertion event are part of a single exercise event.
4. The method of claim 1 , further comprising:
subsequent to the first period of time, storing the one or more dynamic physiological response models for the subject in a data base; and
prior to the second period of time, retrieving the one or more dynamic physiological response models for the subject from the data base.
5. The method of claim 4 , wherein the first exertion event is performed on a first exercise equipment and the second exertion event is performed on a second exercise equipment.
6. The method of claim 1 , wherein the first exertion event is performed on an exercise equipment and the one or more of the exertion monitors include an exertion monitor incorporated into the exercise equipment.
7. The method of claim 1 , wherein the first exertion event is performed on an exercise equipment and the one or more of the physiological response monitors include a physiological response monitor incorporated into the exercise equipment.
8. The method of claim 1 , wherein the one or more of the exertion monitors include an exertion monitor incorporated into a user wearable device.
9. The method of claim 1 , wherein the one or more of the physiological response monitors include a physiological response monitor incorporated into a user wearable device.
10. The method of claim 1 , wherein the one or more of the exertion monitors include an inertial measurement unit.
11. The method of claim 1 , wherein the one or more of the exertion monitors include one or more of an optical tracking monitor, an acoustical tracking monitor, or a radio frequency (RF) signal monitor.
12. The method of claim 1 , wherein the first exertion event is performed on an exercise equipment, the method further comprising:
identifying and authorizing a type of exercise equipment from one or both of the received one or more exertion signals and the received one or more physiological signals.
13. The method of claim 1 , further comprising:
identifying an activity performed by the subject during the first exertion event.
14. The method of claim 1 , wherein training the one or more dynamic physiological response models for the subject further comprises:
comparing an electronic electrocardiography (ECG) signal from the one or more physiological response monitors with generated exertion based values; and
computing a signal error between the electronic ECG signal and the generated exertion based values.
15. The method of claim 1 , wherein changing the exercise operating parameters for the exercise equipment includes changing a speed value for the exercise equipment.
16. The method of claim 1 , wherein changing the exercise operating parameters for the exercise equipment includes changing an incline value for the exercise equipment.
17. The method of claim 1 , wherein changing the exercise operating parameters for the exercise equipment includes changing a resistance value for the exercise equipment.
18. The method of claim 1 , further comprising:
during the second exertion event for the subject, presenting the synthetically generated heart rate signal to the subject on a display of exercise equipment on which the second exertion event is performed.
19. The method of claim 1 , wherein the one or more dynamic physiological response models for the subject are a plurality of models for the subject, the method further comprising:
storing the one or more dynamic physiological response models for the subject in a database additionally storing physiological response models for other subjects; and
wherein generating the synthetically generated heart rate signal of the subject includes:
identifying the subject; and
retrieving dynamic physiological response models for the identified subject.