METHOD AND SYSTEM FOR USE OF TELEMEDICINE-ENABLED REHABILITATIVE EQUIPMENT FOR PREDICTION OF SECONDARY DISEASE

1 . A computer-implemented system, comprising:

an electromechanical device configured to be manipulated by a user while the user is performing a treatment plan; and

a first computing device configured to:

receive treatment data pertaining to the user while the user uses the electromechanical device to perform the treatment plan;

identify at least one aspect of the treatment data pertaining to the user associated with a first device mode of the electromechanical device; and

in response to a determination that the at least one aspect of the treatment data pertaining to the user associated with the first device mode of the electromechanical device is correlated with at least one second condition of the user, generate second condition information indicating at least the second condition; and

control, based on the second condition information, operation of the electromechanical device by transmitting a control instruction to the electromechanical device, wherein the electromechanical device comprises at least one pedal.

2 . The computer-implemented system of claim 1 , wherein the first computing device is further configured to determine whether the at least one aspect of the treatment data pertaining to the user associated with the first electromechanical device mode of the electromechanical device further includes instructions that cause the processor to:

write to an associated memory, configured to be accessed by an artificial intelligence engine, the at least one aspect of the treatment data pertaining to the user associated with the first electromechanical device mode of the electromechanical device, the artificial intelligence engine being configured to use at least one machine learning model to generate, using the at least one aspect of the treatment data pertaining to the user associated with the first electromechanical device mode of the electromechanical device, at least one of second condition output prediction; and

receive, from the artificial intelligence engine, the at least one second condition output prediction.

3 . The computer-implemented system of claim 2 , wherein the artificial intelligence engine is further configured to use at least one machine learning model to generate, further using second condition information associated with other users, the at least one of second condition output prediction.

4 . A method comprising:

receiving treatment data pertaining to a user using a electromechanical device to perform a treatment plan;

identifying at least one aspect of the treatment data pertaining to the user associated with a first device mode of the electromechanical device;

in response to a determination that the at least one aspect of the treatment data pertaining to the user associated with the first device mode of the electromechanical device is correlated with at least one second condition of the user, generating second condition information indicating at least the second condition; and

controlling, based on the second condition information, operation of the electromechanical device by transmitting a control instruction to the electromechanical device, wherein the electromechanical device comprises at least one pedal.

5 . The method of claim 4 , wherein determining whether the at least one aspect of the treatment data pertaining to the user associated with the first device mode of the electromechanical device is correlated with the at least one second condition includes:

writing to an associated memory, configured to be accessed by an artificial intelligence engine, the at least one aspect of the treatment data pertaining to the user associated with the first device mode of the electromechanical device, the artificial intelligence engine being configured to use at least one machine learning model to generate, using the at least one aspect of the treatment data pertaining to the user associated with the first device mode of the electromechanical device, at least one of second condition output prediction; and

receiving, from the artificial intelligence engine, the at least one second condition output prediction.

6 . The method of claim 5 , wherein the artificial intelligence engine is further configured to use at least one machine learning model to generate, further using second condition information associated with other users, the at least one of second condition output prediction.

7 . The method of claim 6 , wherein the user and one or more other users belong to a user cohort.

8 . The method of claim 5 , wherein the at least one machine learning model includes a deep network comprising multiple levels of non-linear operations.

9 . The method of claim 4 , wherein at least some of the treatment data corresponds to at least some sensor data from a sensor associated with the treatment device.

10 . The method of claim 4 , wherein at least some of the treatment data corresponds to at least some sensor data from a sensor associated with a wearable device worn by the user while using the electromechanical device.

11 . The method of claim 4 , wherein at least some of the treatment data includes, while the user uses the electromechanical device, at least one of a vital sign of the user, a respiration rate of the user, a heartrate of the user, a temperature of the user, an SpO2-measurement of the blood oxygen level of the user, and a blood pressure of the user.

12 . The method of claim 4 , wherein the first device mode of the electromechanical device includes one of an active assist treatment device mode, a passive mode, an active-assisted mode, a resistive mode, and an active mode.

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

receive treatment data pertaining to a user using a electromechanical device to perform a treatment plan;

identify at least one aspect of the treatment data pertaining to the user associated with a first device mode of the electromechanical device;

in response to a determination that the at least one aspect of the treatment data pertaining to the user associated with the first device mode of the electromechanical device is correlated with at least one second condition of the user, generate second condition information indicating at least the second condition; and

control, based on the second condition information, operation of the electromechanical device by transmitting a control instruction to the electromechanical device, wherein the electromechanical device comprises at least one pedal.

14 . The computer-readable medium of claim 13 , wherein the instructions causing the processing device to determine whether the at least one aspect of the treatment data pertaining to the user associated with the first device mode of the electromechanical device further includes instructions that cause the processing device to:

write to an associated memory, configured to be accessed by an artificial intelligence engine, the at least one aspect of the treatment data pertaining to the user associated with the first device mode of the electromechanical device, the artificial intelligence engine being configured to use at least one machine learning model to generate, using the at least one aspect of the treatment data pertaining to the user associated with the first device mode of the electromechanical device, at least one of second condition output prediction; and

receive, from the artificial intelligence engine, the at least one second condition output prediction.

15 . The computer-readable medium of claim 14 , wherein the artificial intelligence engine is further configured to use at least one machine learning model to generate, further using second condition information associated with other users, the at least one of second condition output prediction.

16 . The computer-readable medium of claim 15 , wherein the user and one or more other users belong to a user cohort.

17 . The computer-readable medium of claim 14 , wherein the at least one machine learning model includes a deep network comprising multiple levels of non-linear operations.

18 . The computer-readable medium of claim 13 , wherein at least some of the treatment data corresponds to at least some sensor data from a sensor associated with the electromechanical device.

19 . The computer-readable medium of claim 13 , wherein at least some of the treatment data corresponds to at least some sensor data from a sensor associated with a wearable device worn by the user while using the electromechanical device.

20 . The computer-readable medium of claim 13 , wherein at least some of the treatment data, while the user uses the electromechanical device, at least one of a vital sign of the user, a respiration rate of the user, a heartrate of the user, a temperature of the user, an SpO2-measurement of a blood oxygen level of the user, and a blood pressure of the user.