Augmented reality placement of goniometer or other sensors
Systems and methods for positioning one or more sensors on a user. The system has user sensors, apparatus sensors, and treatment sensors. A processing device, executing computer readable instructions stored in a memory, cause the processing device to: generate an enhanced environment representative of an environment; receive apparatus data representative of a location of the apparatus in the environment; generate an apparatus avatar in the enhanced environment; receive user data representative of a location of the user in the environment; generate a user avatar in the enhanced environment; receive treatment data representative of one or more locations of the treatment sensors in the environment; generate, treatment sensor avatars in the enhanced environment; calculate a treatment location for each treatment sensor, wherein the treatment location is associated with an anatomical structure of a user; and generate instruction data representing an instruction for positioning the treatment sensors at the treatment location.
1. A computer-implemented system, comprising:
a device configured to be used by a user while performing an exercise session;
an interface configured to present content to the user;
a computing device configured to:
receive data pertaining to a user, wherein the data comprises one or more characteristics of the user;
assign, based on the data, the user to a cohort representing people having one or more similarities to at least some of the one or more characteristics of the user;
generate, based on the user being assigned to the cohort, an exercise plan for the user to perform using the device, wherein the exercise plan is generated by a machine learning model trained to generate exercise plans for cohorts; and
based on the exercise plan, controlling, via the machine learning model, operation of the device by using a transmitted control instruction to change an operating parameter of the device.
2. The computer-implemented system of claim 1 , wherein the computing device is configured to transmit the exercise plan for presentation on the interface.
3. The computer-implemented system of claim 1 , wherein the machine learning model is trained to generate the exercise plan in real-time or near real-time.
4. The computer-implemented system of claim 1 , wherein the computing device is configured to provide instructions on via the interface, wherein the visual instructions guide the user to place a sensing device on a portion of the user's body.
5. The computer-implemented system of claim 4 , wherein the sensing device comprises a goniometer, a wearable device, or both.
6. The computer-implemented system of claim 1 , wherein the device comprises one of a mirror, a reflective surface, a projective capability, or some combination thereof.
7. The computer-implemented system of claim 1 , wherein the device is a treadmill.
8. The computer-implemented system of claim 1 , wherein the device is an electromechanical spin-wheel.
9. The computer-implemented system of claim 1 , wherein the device is an electromechanical bicycle.
10. The computer-implemented system of claim 1 , wherein the user data comprises information pertaining to an electronic medical record of the user.
11. A method for assigning a user to a cohort based on one or more characteristics of the user, the method comprising, at a server device:
receiving data pertaining to the user, wherein the data comprises the one or more characteristics of the user;
assigning, based on the data, the user to a cohort representing people having similarities to at least some of the one or more characteristics of the user;
generating, based on the user being assigned to the cohort, an exercise plan for the user to perform using a device, wherein the exercise plan is generated by a machine learning model trained to generate exercise plans for cohorts; and
based on the exercise plan, controlling, via the machine learning model, operation of the device by using a transmitted control instruction to change an operating parameter of the device.
12. The method of claim 11 , further comprising transmitting the exercise plan for presentation as the content on an interface.
13. The method of claim 11 , wherein the machine learning model is trained to generate the exercise plan in real-time or near real-time.
14. The method of claim 11 , further comprising providing visual instructions on an interface, wherein the visual instructions guide the user to place a sensing device on a portion of a body of the user.
15. The method of claim 14 , wherein the sensing device comprises a goniometer, a wearable device, or both.
16. The method of claim 11 , wherein the device comprises one of a mirror, a reflective surface, a projective capability, or some combination thereof.
17. The method of claim 11 , wherein the device is a treadmill.
18. The method of claim 11 , wherein the device is an electromechanical spinwheel.
19. The method of claim 11 , wherein the device is an electromechanical bicycle.
20. The method of claim 11 , wherein the user data comprises information pertaining to an electronic medical record of the user.
21. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:
receive data pertaining to a user, wherein the data comprises one or more characteristics of the user;
assign, based on the data, the user to a cohort representing people having similarities to at least some of the one or more characteristics of the user;
generate, based on the user being assigned to the cohort, an exercise plan for the user to perform using a device, wherein the exercise plan is generated by a machine learning model trained to generate exercise plans for cohorts; and
based on the exercise plan, controlling, via the machine learning model, operation of the device by using a transmitted control instruction to change an operating parameter of the device.
22. The computer-readable medium of claim 21 , wherein the processing device is configured to transmit the exercise plan for presentation as the content on an interface.
23. The computer-readable medium of claim 21 , wherein the machine learning model is trained to generate the exercise plan in real-time or near real-time.
24. The computer-readable medium of claim 21 , wherein the processing device is configured to provide visual instructions on an interface, wherein the visual instructions guide the user to place a sensing device on a portion of a body of the user.
25. The computer-readable medium of claim 24 , wherein the sensing device comprises a goniometer, a wearable device, or both.
26. The computer-readable medium of claim 21 , wherein the device comprises one of a mirror, a reflective surface, a projective capability, or some combination thereof.
27. The computer-readable medium of claim 21 , wherein the device is a treadmill.
28. The computer-readable medium of claim 21 , wherein the device is an electromechanical spin-wheel.
29. The computer-readable medium of claim 21 , wherein the processing device:
receive second data pertaining to the user, wherein the data comprises one or more second characteristics of the user;
reassign, based on the second data, the user to a second cohort representing second people having one or more second similarities to at least some of the one or more second characteristics of the user; and
generate, based on the user being assigned to the second cohort, a second exercise plan for the user to perform using the device, wherein the second exercise plan is generated by a machine learning model trained to generate the exercise plans for the cohorts.
30. An apparatus, comprising:
a memory device storing instructions; and
a processing device communicatively coupled to the memory device, wherein the processing device is configured to execute the instructions to:
receive data pertaining to a user, wherein the data comprises one or more characteristics of the user;
assign, based on the data, the user to a cohort representing people having similarities to at least some of the one or more characteristics of the user;
generate, based on the user being assigned to the cohort, an exercise plan for the user to perform using a device, wherein the exercise plan is generated by a machine learning model trained to generate exercise plans for cohorts; and
based on the exercise plan, controlling, via the machine learning model, operation of the device by using a transmitted control instruction to change an operating parameter of the device.