Method and system for implementing dynamic treatment environments based on patient information

1. A computer-implemented system, comprising:

a treatment apparatus configured to be used by a user while performing an exercise session;

a patient interface configured to present content to the user;

a device located proximate to the treatment apparatus;

a computing device configured to:

receive user data related to the exercise session;

generate, based on the user data, a modified treatment plan, wherein the modified treatment plan is configured to:

modify a first operational aspect of the patient interface, wherein the first operational aspect pertains to audiovisual; and

wherein, while the user performs the exercise session using the treatment apparatus, the computing device is further configured to control, using the modified treatment plan, the treatment apparatus, wherein controlling the treatment apparatus comprises modifying a second operational aspect of the device, and, further, wherein the second operational aspect pertains to an environment, and, further wherein the environment comprises one or more augmented reality parameters.

2. The computer-implemented system of claim 1 , wherein the computing device is further to generate, via an artificial intelligence engine, a machine learning model trained to generate the modified treatment plan.

3. The computer-implemented system of claim 1 , wherein the treatment

apparatus comprises one of a mirror, a reflective surface, a projective capability, or some combination thereof.

4. The computer-implemented system of claim 1 , wherein the treatment apparatus is a treadmill.

5. The computer-implemented system of claim 1 , wherein the treatment apparatus is an electromechanical spin-wheel.

6. The computer-implemented system of claim 1 , wherein the treatment apparatus is an electromechanical bicycle.

7. The computer-implemented system of claim 1 , wherein the device is a light source and the second operational aspect comprises a brightness, a color tone, a contrast, a pattern, or some combination thereof.

8. The computer-implemented system of claim 1 , wherein the device is a speaker and the second operational aspect comprises a volume, a dynamic range, an audio stream, a spoken language, or some combination thereof.

9. The computer-implemented system of claim 1 , wherein the first operational aspect comprises an augmented video session.

10. The computer-implemented system of claim 1 , wherein the user data comprises cohort-related data.

11. The computer-implemented system of claim 1 , wherein the computing device is configured to execute one or more machine learning models to group the user into a cohort based on the user data.

12. The computer-implemented system of claim 1 , wherein the computing device is configured to generate, using one or more machine learning models, the modified treatment plan in real-time or near real-time.

13. The computer-implemented system of claim 1 , wherein the user data comprises employment experiences, medical history, or both.

14. A method for implementing dynamic exercise environments, the method comprising, at a server device:

receiving user data related to an exercise session;

generating, based on the user data, a modified treatment plan; and

transmitting the modified treatment plan to a treatment apparatus accessible to a user, to a patient interface, to a device, or some combination thereof, wherein the modified treatment plan is configured to:

modify a first operational aspect of the patient interface, wherein the operational aspect pertains to audiovisual; and

wherein, while the user performs the exercise using the treatment apparatus, the method further comprises controlling, using the device and the modified treatment plan, the treatment apparatus, wherein controlling the treatment apparatus comprises modifying a second operational aspect of the device, and, further, wherein the second operational aspect pertains to an environment, and, further, wherein the environment comprises one or more augmented reality parameters.

15. The method of claim 14 , comprising generating, via an artificial intelligence engine, a machine learning model trained to generate the modified treatment plan.

16. The method of claim 14 , wherein the treatment apparatus comprises one of a mirror, a reflective surface, a projective capability, or some combination thereof.

17. The method of claim 14 , wherein the treatment apparatus is a treadmill.

18. The method of claim 14 , wherein the treatment apparatus is an electromechanical spin-wheel.

19. The method of claim 14 , wherein the treatment apparatus is an electromechanical bicycle.

20. The method of claim 14 , wherein the device is a light source and the second operational aspect comprises a brightness, a color tone, a contrast, a pattern, or both.

21. The method of claim 14 , wherein the device is a speaker and the second operational aspect comprises a volume, a dynamic range, an audio stream, a spoken language, or some combination thereof.

22. The method of claim 14 , wherein the first audiovisual operational aspect comprises an augmented video session.

23. The method of claim 14 , wherein the user data comprises cohort-related data.

24. The method of claim 14 , wherein the server device is configured to execute one or more machine learning models to group the user into a cohort based on the user data.

25. The method of claim 14 , wherein the server device is configured to generate, using one or more machine learning models, the modified treatment plan in real-time or near real-time.

26. The method of claim 14 , wherein the user data comprises employment experiences, medical history, or both.

27. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:

receive user data related to an exercise session;

generate, based on the user data, a modified treatment plan, wherein the modified treatment plan is configured to:

modify a first operational aspect of a patient interface, wherein the first operational aspect pertains to audiovisual; and

wherein the instructions are further executable to, while the user performs the exercise session using a treatment apparatus, cause the processing device to control, using the modified treatment plan, a treatment apparatus, wherein controlling the treatment apparatus comprises modifying a second operational aspect of the device, and, further, wherein the second operational aspect pertains to an environment, and, further, wherein the environment comprises one or more augmented reality parameters.

28. The computer-readable medium of claim 27 , wherein the processing device is configured to generate, via an artificial intelligence engine, a machine learning model trained to generate the modified treatment plan.

29. The computer-readable medium of claim 28 , wherein the processing device is configured to generate, using the machine learning model, the modified treatment plan in real-time or near real-time.

30. An apparatus, comprising:

a memory device storing instructions; and

a processing device communicatively coupled to the memory device, wherein the processing device executes the instructions to:

receive user data related to an exercise session;

generate, based on the user data, a modified treatment plan, wherein the modified treatment plan is configured to:

modify a first operational aspect of a patient interface, wherein the first operational aspect pertains to audiovisual; and

wherein the processing device is further configured to, while the user performs the exercise session using a treatment apparatus, control, using the modified treatment plan, the treatment apparatus, wherein controlling the treatment apparatus comprises modifying a second operational aspect of the device, and, further, wherein the second operational aspect pertains to an environment, and, further, wherein the environment comprises one or more augmented reality parameters.