Method and System Using Artificial Intelligence to Monitor User Characteristics During A Telemedicine Session
A computer-implemented system may include a treatment device configured to be manipulated by a user while the user is performing a treatment plan and a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session. The computer-implemented system may also include a first computing device configured to: receive treatment data pertaining to the user while the user uses the treatment device to perform the treatment plan; write to an associated memory, for access by an artificial intelligence engine, the treatment data; receive, from the artificial intelligence engine, at least one prediction; identify a threshold corresponding to the at least one prediction; and, in response to a determination that the at least one prediction is outside of the range of the threshold, update the treatment data pertaining to the user to indicate the at least one prediction.
1 . A computer-implemented system, comprising:
an electromechanical machine 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 machine to perform the treatment plan;
generate, via an artificial intelligence engine, at least one prediction, wherein the artificial intelligence engine uses at least one aspect of the treatment data;
identify a threshold corresponding to the at least one prediction; and
in response to a determination that the at least one prediction is outside of a range of the threshold, update the treatment data pertaining to the user to include the at least one prediction.
2 . The computer-implemented system of claim 1 , wherein the first computing device is further configured to receive, from an interface of a second computing device of a healthcare provider, treatment plan input, wherein the treatment plan input includes at least one modification to the treatment plan.
3 . The computer-implemented system of claim 2 , wherein the first computing device is further configured to modify the treatment plan using the at least one modification indicated in the treatment plan input.
4 . A method comprising:
receiving data associated with one or more physical or mental actions performed by a user who uses an electromechanical machine to perform the actions;
generating, via an artificial intelligence engine, at least one prediction, wherein the artificial intelligence engine uses at least one aspect of the data;
identifying a threshold corresponding to the at least one prediction; and
in response to a determination that the at least one prediction is outside of a range of the threshold, updating the data to include the at least one prediction.
5 . The method of claim 4 , further comprising receiving, from an interface of a computing device of a healthcare provider, input associated with the one or more physical or mental actions, wherein the input includes at least one modification to the actions.
6 . The method of claim 5 , further comprising modifying the actions using the at least one modification indicated in the input.
7 . The method of claim 6 , further comprising controlling, while the user uses the electromechanical machine during a telemedicine session and based on the modified actions, the electromechanical machine.
8 . The method of claim 4 , wherein at least some of the data corresponds to sensor data from a sensor associated with the electromechanical machine.
9 . The method of claim 4 , wherein at least some of the data corresponds to sensor data from a sensor associated with the user while using the electromechanical machine.
10 . The method of claim 9 , wherein the sensor associated with the user is comprised of a wearable device worn by the user.
11 . The method of claim 4 , wherein the data comprises at least baseline measurement information including, while the user is at rest, 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, a blood pressure of the user, a blood oxygen saturation level of the user, a blood glucose level of the user, the eye dilation level of the user, a biomarker level of the user, and wherein the data also comprises measurement information including, while the user performs the treatment plan, 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, a blood pressure of the user, a blood oxygen saturation level of the user, a blood glucose level of the user, the eye dilation level of the user, and a biomarker level of the user.
12 . The method of claim 4 , wherein the artificial intelligence engine uses at least one machine learning model including a deep network comprising more than one level of non-linear operations.
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 who uses an electromechanical machine to perform a treatment plan;
generate, via an artificial intelligence engine, at least one prediction, wherein the artificial intelligence engine uses at least one aspect of the treatment data;
identify a threshold corresponding to the at least one prediction; and
in response to a determination that the at least one prediction is outside of a range of the threshold, update the treatment data pertaining to the user to include the at least one prediction.
14 . The computer-readable medium of claim 13 , wherein the instructions further cause the processing device to receive, from an interface of a computing device of a healthcare provider, treatment plan input, wherein the treatment plan input includes at least one modification to the treatment plan.
15 . The computer-readable medium of claim 14 , wherein the instructions further cause the processing device to modify the treatment plan using the at least one modification indicated in the treatment plan input.
16 . The computer-readable medium of claim 15 , wherein the instructions further cause the processing device to control, while the user uses the electromechanical machine during a telemedicine session and based on the modified treatment plan, the electromechanical machine.
17 . The computer-readable medium of claim 13 , wherein at least some of the treatment data corresponds to sensor data from a sensor associated with the treatment device.
18 . The computer-readable medium of claim 13 , wherein at least some of the treatment data corresponds to sensor data from a sensor associated with the user while using the electromechanical machine.
19 . The computer-readable medium of claim 18 , wherein the sensor associated with the user is comprised of a wearable device worn by the user.
20 . The computer-readable medium of claim 13 , wherein the treatment data comprises baseline measurement information including, while the user is at rest, 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, a blood pressure of the user, a blood oxygen saturation level of the user, a blood glucose level of the user, the eye dilation level of the user, and a biomarker level of the user, and wherein the treatment data comprises measurement information including, while the user performs the treatment plan, 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, a blood pressure of the user, a blood oxygen saturation level of the user, a blood glucose level of the user, the eye dilation level of the user, and a biomarker level of the user.