Method and system for using artificial intelligence to onboard a user for an exercise plan

1 . A computer-implemented method for using an artificial intelligence engine to onboard a user for an exercise plan, wherein the computer-implemented method comprises:

generating, by the artificial intelligence engine, a machine learning model trained to receive, as input, (i) onboarding data associated with at least one use of an exercise device by the user and (ii) an onboarding protocol that comprises at least one baseline fitness test;

receiving a physical activity goal and a pain level of the user;

performing, in response to receiving the physical activity goal and the pain level of the user, the onboarding protocol, wherein the onboarding protocol comprises:

obtaining, while the user performs, using the exercise device, a plurality of exercises of the baseline fitness test of the onboarding protocol, the onboarding data, wherein the onboarding data includes at least one of (i) a force exerted by the user on the exercise device while the user performs the plurality of exercises of the baseline fitness test and (ii) an operating characteristic of a component of the exercise device, wherein the operating characteristic is obtained while the exercise device is used by the user to perform the plurality of exercises of the baseline fitness test, and wherein the onboarding data corresponds to a set of conditions required to enable the user to, subsequent to completing the baseline fitness test, begin using the exercise device to perform the exercise plan;

generating, based on the onboarding data and the onboarding protocol and using the machine learning model, the exercise plan;

determining, based on the onboarding data and the onboarding protocol and using the machine learning model, a fitness level of the user, wherein:

the plurality of exercises of the baseline fitness test comprises exercises with tiered difficulty levels,

the onboarding protocol increases, by controlling at least one operating characteristic of the exercise device, a difficulty level for a subsequent exercise of the plurality of exercises of the baseline fitness test when the user completes a previous exercise of the plurality of exercises of the baseline fitness test, and

based on a completion state of a last exercise of the plurality of exercises of the baseline fitness test performed by the user, the fitness level of the user is determined;

selecting a difficulty level for each of a plurality of exercises of the exercise plan;

based on the exercise plan and the fitness level of the user, predicting a length of time for the user to achieve the physical activity goal by following the exercise plan;

based on the exercise plan, controlling the exercise device as the user performs the exercise plan;

as the user performs the exercise plan by using the exercise device, receiving one or more measurements; and

based on the one or more measurements, updating the fitness level of the user and the length of time for the user to achieve the physical activity goal.

2 . The computer-implemented method of claim 1 , wherein the exercises are associated with a plurality of domains comprising range of motion, strength, balance, endurance, mobility, stability, pliability, flexibility, or some combination thereof.

3 . The computer-implemented method of claim 1 , further comprising:

as the user performs the exercise plan, receiving one or more measurements;

based on the one or more measurements, modifying a portion of the exercise plan to include different exercises.

4 . The computer-implemented method of claim 1 , further comprising:

receiving feedback from the user, wherein the feedback pertains to the pain level of the user, an enjoyment level of the user performing the exercise plan, or some combination thereof; and

based on the feedback, modifying the exercise plan to include different exercises.

5 . The computer-implemented method of claim 1 , further comprising:

presenting a virtual character on a computing device associated with the exercise device, wherein the virtual character comprises a coach that provides instructions as to how to properly perform an exercise.

6 . The computer-implemented method of claim 5 , wherein the coach is a virtual avatar.

7 . The computer-implemented method of claim 5 , wherein the coach is a live person presented in real-time.

8 . The computer-implemented method of claim 5 , wherein the coach is a pre-recorded live person.

9 . The computer-implemented method of claim 1 , wherein the machine learning model is trained to generate the exercise plan to reduce the pain level of the user, a dependency of the user on a certain medication, or some combination thereof.

10 . The computer-implemented method of claim 1 , further comprising presenting the exercise plan on a computing device associated with the exercise device.

11 . The computer-implemented method of claim 1 , further comprising:

receiving input from the user, wherein the input comprises an instruction to modify an operating parameter of the exercise device, and the input is received via a microphone, a touchscreen, a keyboard, a mouse, a haptic signal, or some combination thereof.

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

generating, by an artificial intelligence engine, a machine learning model trained to receive, as input, (i) onboarding data associated with at least one use of an exercise device by a user and (ii) an onboarding protocol that comprises at least one baseline fitness test;

receiving a physical activity goal and a pain level of the user;

performing, in response to receiving the physical activity goal and the pain level of the user, the onboarding protocol, wherein the onboarding protocol comprises:

obtaining, while the user performs, using the exercise device, a plurality of exercises of the baseline fitness test of the onboarding protocol, the onboarding data, wherein the onboarding data includes at least one of (i) a force exerted by the user on the exercise device while the user performs the plurality of exercises of the baseline fitness test and (ii) an operating characteristic of a component of the exercise device, wherein the operating characteristic is obtained while the exercise device is used by the user to perform the plurality of exercises of the baseline fitness test, and wherein the onboarding data corresponds to a set of conditions required to enable the user to, subsequent to completing the baseline fitness test, begin using the exercise device to perform an exercise plan;

generate, based on the onboarding data and the onboarding protocol and using the machine learning model, the exercise plan;

determine a safety limit based on a disease protocol associated with the user;

in order to output an exercise plan, use the machine learning model to process the onboarding data and the onboarding protocol;

determine, based on the onboarding data and the onboarding protocol and using the machine learning model, a fitness level of the user, wherein:

the plurality of exercises of the baseline fitness test comprises exercises with tiered difficulty levels,

when the user completes a previous exercise of the plurality of exercises of the baseline fitness test, the onboarding protocol increases, by controlling at least one operating characteristic of the exercise device, a difficulty level for a subsequent exercise of the plurality of exercises of the baseline fitness test, and

based on a completion state of a last exercise of the plurality of exercises of the baseline fitness test performed by the user, the fitness level of the user is determined; and

select a difficulty level for each of a plurality of exercises of the exercise plan; and

based on the exercise plan, control the exercise device as the user performs the exercise plan by using the exercise device.

13 . The computer-readable medium of claim 12 , wherein the exercises are associated with a plurality of domains comprising range of motion, strength, balance, endurance, mobility, stability, pliability, flexibility, or some combination thereof.

14 . The computer-readable medium of claim 12 , wherein the processing device is configured to:

as the user performs the exercise plan, receive one or more measurements;

based on the one or more measurements, modifying a portion of the exercise plan to include different exercises.

15 . The computer-readable medium of claim 12 , wherein the processing device is configured to:

receive feedback from the user, wherein the feedback pertains to the pain level of the user, an enjoyment level of the user performing the exercise plan, or some combination thereof; and

based on the feedback, modify the exercise plan to include different exercises.

16 . The computer-readable medium of claim 12 , wherein the processing device is configured to:

present a virtual character on a computing device associated with the exercise device, wherein the virtual character comprises a coach that provides instructions as to how to properly perform an exercise.

17 . The computer-readable medium of claim 16 , wherein the coach is a virtual avatar.

18 . The computer-readable medium of claim 16 , wherein the coach is a live person presented in real-time.

19 . The computer-implemented method of claim 16 , wherein the coach is a pre-recorded live person.

20 . A system comprising:

a memory device storing instructions;

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

generate, by an artificial intelligence engine, a machine learning model trained to receive, as input, (i) onboarding data associated with at least one use of an exercise device by a user and (ii) an onboarding protocol that comprises at least one baseline fitness test;

receiving a physical activity goal and a pain level of the user;

performing, in response to receiving the physical activity goal and the pain level of the user, the onboarding protocol, wherein the onboarding protocol comprises:

obtaining, while the user performs, using the exercise device, a plurality of exercises of the baseline fitness test of the onboarding protocol, the onboarding data, wherein the onboarding data includes at least one of (i) a force exerted by the user on the exercise device while the user performs the plurality of exercises of the baseline fitness test and (ii) an operating characteristic of a component of the exercise device, wherein the operating characteristic is obtained while the exercise device is used by the user to perform the plurality of exercises of the baseline fitness test, and wherein the onboarding data corresponds to a set of conditions required to enable the user to, subsequent to completing the baseline fitness test, begin using the exercise device to perform an exercise plan;

generate, based on the onboarding data and the onboarding protocol and using the machine learning model, the exercise plan;

determine, based on the onboarding data and the onboarding protocol and using the machine learning model, a fitness level of the user, wherein:

the plurality of exercises of the baseline fitness test comprises exercises with tiered difficulty levels,

the onboarding protocol increases, by controlling at least one operating characteristic of the exercise device, a difficulty level for a subsequent exercise of the plurality of exercises of the baseline fitness test when the user completes a previous exercise of the plurality of exercises of the baseline fitness test, and

based on a completion state of a last exercise of the plurality of exercises of the baseline fitness test performed by the user, the fitness level of the user is determined;

select a difficulty level for each of a plurality of exercises of the exercise plan;

based on the exercise plan and the fitness level of the user, predicting a length of time for the user, by following the exercise plan, to achieve the physical activity goal;

based on the exercise plan, control the exercise device as the user performs the exercise plan;

as the user performs the exercise plan by using the exercise device, receive one or more measurements; and

based on the one or more measurements, update the length of time for the user to achieve the physical activity goal.