System and method for using artificial intelligence in telemedicine-enabled hardware to optimize rehabilitative routines capable of enabling remote rehabilitative compliance
A computer-implemented system comprising a treatment apparatus, a patient interface, and a processing device is disclosed. The processing device is configured to receive treatment data pertaining to the user during the telemedicine session, wherein the treatment data comprises one or more characteristics of the user; determine, via one or more trained machine learning models, at least one respective measure of benefit one or more exercise regimens provide the user, wherein the determining the respective measure of benefit is based on the treatment data; determine, via the one or more trained machine learning models, one or more probabilities of the user complying with the one or more exercise regimens; and transmit the treatment plan to a computing device, wherein the treatment plan is generated based on the one or more probabilities and the respective measure of benefit the one or more exercise regimens provide the user.
1. A computer-implemented system, comprising:
a device configured to be used by a user while performing an exercise plan;
an interface comprising an output device configured to present information associated with a communications session; and
a processing device configured to:
receive data pertaining to the user, wherein the data comprises one or more characteristics of the user;
determine, via one or more trained machine learning models and based on historical data indicating completion of exercises by the user and/or another user, one or more probabilities that the user will comply with one or more exercise regimens, wherein the determining the one or more probabilities is further based on the data; and
transmit the exercise plan, wherein the exercise plan is configured for the user to perform, to a computing device, and further wherein the exercise plan is generated based on the one or more probabilities.
2. The computer-implemented system of claim 1 , wherein the processing device is configured to determine, via the one or more trained machine learning models, at least one respective measure of benefit that the one or more exercise regimens provide the user, wherein the determining the respective measure of benefit is based on the data, and wherein the respective measure of benefit may be an indication of change.
3. The computer-implemented system of claim 1 , wherein the processing device is further configured to control the device, based on the exercise plan, while the user uses the device.
4. The computer-implemented system of claim 1 , wherein the determining one or more probabilities is further based on:
(i) received feedback from the user, the another user, or both,
(ii) received feedback from the device used by the user, or
(iii) some combination thereof.
5. The computer-implemented system of claim 1 , wherein the processing device is further configured to:
receive user input pertaining to a desired benefit, a manageable pain level, an indication of a probability that the user will comply with a particular exercise regimen, or some combination thereof; and
generate, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is further performed based on the desired benefit, the manageable pain level, the indication of the probability that the user will comply with the particular exercise regimen, or some combination thereof.
6. A computer-implemented, comprising:
a device configured to be used by a user while performing an exercise plan;
an interface comprising an output device configured to present information associated with a communications session; and
a processing device configured to:
receive data pertaining to the user, wherein the data comprises one or more characteristics of the user;
determine, via one or more trained machine learning models, one or more probabilities that the user will comply with one or more exercise regimens, wherein the determining the one or more probabilities is based on the data; and
transmit the exercise plan to a computing device, wherein the exercise plan is generated based on the one or more probabilities,
wherein the processing device is further configured to generate, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is performed based on the one or more probabilities that the user will comply with each of the one or more exercise regimens.
7. The computer-implemented system of claim 6 , wherein the exercise plan is generated using a non-parametric model, a parametric model, or a combination of both the non-parametric model and the parametric model.
8. The computer-implemented system of claim 6 , wherein the exercise plan is generated using a probability density function, a Bayesian prediction model, a Markovian prediction model, or any other mathematically-based prediction model.
9. The computer-implemented system of claim 1 , wherein the processing device is further configured to:
generate the exercise plan, wherein the generating is based on a plurality of factors comprising an amount of sleep associated with the user, information pertaining to a diet of the user, information pertaining to an eating schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, information pertaining to a microbiome from one or more locations on or in the user, an indication of an energy level of the user, or some combination thereof.
10. The computer-implemented system of claim 1 , wherein the data further comprises one or more characteristics of the device.
11. A computer-implemented method for optimizing an exercise plan for a user to perform using a device, the computer-implemented method comprising:
receiving data pertaining to the user, wherein the data comprises one or more characteristics of the user;
determining, via one or more trained machine learning models and based on historical data indicating completion of exercises by the user and/or another user, one or more probabilities that the user will comply with one or more exercise regimens; and
transmitting the exercise plan, wherein the exercise plan is configured for the user to perform, to a computing device, and further wherein the exercise plan is generated based on the one or more probabilities.
12. The computer-implemented method of claim 11 , further comprising determining, via the one or more trained machine learning models, at least one respective measure of benefit the one or more exercise regimens provide the user, wherein the determining the respective measure of benefit is based on the data, and wherein the measure of benefit may be an indication of change.
13. The computer-implemented method of claim 11 , further comprising controlling, based on the exercise plan, the device while the user uses the device.
14. The computer-implemented method of claim 11 , wherein the determining the one or more probabilities is further based on:
(i) received feedback from the user, the another user, or both,
(ii) received feedback from the device used by the user, or
(iii) some combination thereof.
15. The computer-implemented method of claim 11 , further comprising:
receiving user input pertaining to a desired benefit, a manageable pain level, an indication of a probability that the user will comply with a particular exercise regimen, or some combination thereof; and
generating, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is further performed based on the desired benefit, the manageable pain level, the indication of the probability that the user will comply with the particular exercise regimen, or some combination thereof.
16. The computer-implemented method of claim 11 , further comprising generating, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is performed based on the one or more probabilities that the user will comply with each of the one or more exercise regimens.
17. The computer-implemented method of claim 16 , wherein, during generation of the exercise plan, the probability of the user complying is weighted more heavily or less heavily than another factor.
18. The computer-implemented method of claim 16 , wherein the exercise plan is generated using a non-parametric model, a parametric model, or a combination of both the non-parametric model and the parametric model.
19. The computer-implemented method of claim 16 , wherein the exercise plan is generated using a probability density function, a Bayesian prediction model, a Markovian prediction model, or any other mathematically-based prediction model.
20. The computer-implemented method of claim 11 , further comprising:
generating the exercise plan, wherein the generating the exercise plan based on a plurality of factors comprising an amount of sleep associated with the user, information pertaining to a diet of the user, information pertaining to an eating schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, information pertaining to a microbiome from one or more locations on or in the user, an indication of an energy level of the user, or some combination thereof.
21. The computer-implemented method of claim 11 , wherein the data further comprises one or more characteristics of the device.
22. A 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;
determine, via one or more trained machine learning models and based on historical data indicating completion of exercises by the user and/or another user, one or more probabilities that the user will comply with one or more exercise regimens; and
transmit an exercise plan, wherein the exercise plan is configured for the user to perform, to a computing device, and further wherein the exercise plan is generated based on the one or more probabilities.
23. The computer-readable medium of claim 22 , wherein the processing device is configured to determine, via the one or more trained machine learning models, at least one respective measure of benefit the one or more exercise regimens provide the user, wherein the determining the respective measure of benefit is based on the data, and wherein the measure of benefit may be positive or negative.
24. The computer-readable medium of claim 22 , wherein the processing device is further configured to control, based on the exercise plan, a device while the user uses the device.
25. The computer-readable medium of claim 22 , wherein the determining the one or more probabilities is further based on:
(i) received feedback from the user, the another user, or both,
(ii) received feedback from the device used by the user, or
(iii) some combination thereof.
26. The computer-readable medium of claim 22 , wherein the processing device is further configured to:
receive user input pertaining to a desired benefit, a manageable pain level, an indication of a probability that the user will comply with a particular exercise regimen, or some combination thereof; and
generate, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is further performed based on the desired benefit, the manageable pain level, the indication of the probability that the user will comply with the particular exercise regimen, or some combination thereof.
27. The computer-readable medium of claim 22 , wherein the processing device is further configured to generate, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is performed based on the one or more probabilities of the user complying with each of the one or more exercise regimens.
28. A system, comprising:
a memory device storing instructions; and
a processing device communicatively coupled to the memory device, the processing device executes the instructions to:
receive data pertaining to a user, wherein the data comprises one or more characteristics of the user;
determine, via one or more trained machine learning models and based on historical data indicating completion of exercises by the user and/or another user, one or more probabilities that the user will comply with one or more exercise regimens; and
transmit an exercise plan, wherein the exercise plan is configured for the user to perform, to a computing device, and further wherein the exercise plan is generated based on the one or more probabilities.
29. The system of claim 28 , wherein the processing device is further configured to:
receive user input pertaining to a desired benefit, a manageable pain level, an indication of a probability that the user will comply with a particular exercise regimen, or some combination thereof; and
generate, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is further performed based on the desired benefit, the manageable pain level, the indication of the probability that the user will comply with the particular exercise regimen, or some combination thereof.
30. The system of claim 28 , wherein the processing device is further configured to generate, using at least a subset of the one or more exercise regimens, the exercise plan for the user to perform using the device, wherein the generating is performed based on the one or more probabilities of the user complying with each of the one or more exercise regimens.