Method and system for using sensors to optimize a user treatment plan in a telemedicine environment
A method for optimizing at least one exercise for a first user. The method includes receiving first user data including attribute data associated with the first user and outcome data associated with the exercise. The method includes generating, based on the first user data, initial target data. The initial target data is associated with at least one of the first user, the exercise apparatus, and the exercise. The method includes receiving measurement data associated with at least one of the first user, the exercise apparatus, and the exercise. The measurement data is associated with one or more sensors. The method includes determining differential data based on one or more differences between the initial target data and the measurement data. The method includes generating, via an artificial intelligence engine and based on the differential data, a machine learning model trained to generate optimized target data.
1 . A method for optimizing at least one exercise for a first user, wherein an exercise apparatus is configured to enable the first user to perform the at least one exercise, the method comprising:
receiving first user data, wherein the first user data includes attribute data associated with the first user and outcome data associated with the exercise, wherein the exercise apparatus comprises at least one pedal that is electronically adjustable to provide at least a minimum desired range of motion;
generating, based on the first user data, initial target data wherein the initial target data is associated with at least one of the first user, the exercise apparatus, and the exercise, wherein the initial target data comprises at least the at least minimum desired range of motion;
receiving measurement data associated with at least one of the first user, the exercise apparatus, and the exercise, wherein the measurement data is associated with one or more sensors;
determining differential data, wherein the determining is based on one or more differences between the initial target data and the measurement data; and
generating, via an artificial intelligence engine and based on the differential data, a machine learning model trained to generate optimized target data.
2 . The method of claim 1 , further comprising controlling, based on the optimized target data, the exercise apparatus.
3 . The method of claim 2 , further comprising transmitting to an interface of the exercise apparatus, a message based on at least one of the differential data and the optimized target data.
4 . The method of claim 2 , wherein controlling the exercise apparatus comprises transmitting, to the exercise apparatus, a signal comprising the optimized target data, wherein the optimized target data includes modification to an operating state of the exercise apparatus.
5 . The method of claim 2 , further comprising:
positioning a first user sensor in an environment proximate to the first user;
positioning a second user sensor adjacent to the first user, and the second user sensor is in communication with the first user sensor; and
receiving, from at least one of the first and second user sensors, user sensor data associated with an attribute of the user, wherein the measurement data comprises the user sensor data.
6 . The method of claim 5 , wherein the attribute of the first user comprises at least one of a measurement of a vital sign of the first user, a respiration rate of the first user, a heartrate of the first user, a heart rhythm of a first user, an oxygen saturation level of the first user, a sugar level of the first user, a composition of blood of the first user, cerebral activity of the first user, cognitive activity of the first user, a lung capacity of the first user, a temperature of the first user, a blood pressure of the first user, an eye movement of the first user, a degree of dilation of an eye of the first user, a reaction time, a sound produced by the first user, a perspiration rate of the first user, an elapsed time of using the exercise apparatus, an amount of force exerted on a portion of the exercise apparatus, a range of motion achieved on the exercise apparatus, a speed measurement of a moving portion of the exercise apparatus, a pressure exerted on a portion of the exercise apparatus, an acceleration measurement of a portion of the exercise apparatus, a jerk of a portion of the exercise apparatus, a torque level of a portion of the exercise apparatus, and an indication of a plurality of pain levels experienced by the first user when using the exercise apparatus.
7 . The method of claim 6 , wherein the message comprises instructions to adjust a position of one or more of the first and second user sensors.
8 . The method of claim 5 , further comprising transmitting to an interface of the exercise apparatus, a message to adjust a position of one or more of the first and second user sensors, wherein the message is based on at least one of the differential data, the initial target data, the user sensor data, and the optimized target data.
9 . The method of claim 5 , wherein the attribute of the exercise apparatus comprises at least one of a resistance, a number of rotations per minute, a time, a weight, a number of repetitions, a force, a pressure, a speed measurement of a moving portion of the exercise apparatus, an acceleration measurement of a portion of the exercise apparatus, a jerk of a portion of the exercise apparatus, a torque level of a portion of the exercise apparatus, and any combination thereof.
10 . The method of claim 2 , further comprising:
receiving, from apparatus sensors associated with the exercise apparatus, apparatus sensor data associated with an attribute of the exercise apparatus, wherein the measurement data comprises the apparatus sensor data.
11 . The method of claim 1 , wherein the differential data is further based on one or more differences between the measurement data and statistical data, wherein the statistical data is associated with at least one of the first user, a second user, the exercise apparatus, and the exercise.
12 . The method of claim 1 , wherein the measurement data is received in real-time or near real-time.
13 . The method of claim 1 , wherein the outcome data is based on a selection by the first user.
14 . The method of claim 1 , wherein, based on the machine learning model, the outcome data is generated by the artificial intelligence engine.
15 . The method of claim 1 , wherein the outcome data includes at least one of a duration of the exercise, a weight, a number of repetitions, a respiration rate of the first user, a heartrate of the first user, a reaction time, a perspiration rate of the first user, an amount of force exerted on a portion of the exercise apparatus, a range of motion achieved on the exercise apparatus, a pressure exerted on a portion of the exercise apparatus, a speed measurement of a moving portion of the exercise apparatus, an acceleration measurement of a portion of the exercise apparatus, a jerk of a portion of the exercise apparatus, a torque level of a portion of the exercise apparatus, a measurement pertaining to an attribute of a second user, and any combination thereof.
16 . A system for optimizing at least one exercise for a first user, wherein an exercise apparatus is configured to enable the first user to perform the at least one exercise, the system comprising:
a processing device; and
a memory including instructions that, when executed by the processing device, cause the processing device to:
receive first user data, wherein the first user data includes attribute data associated with the first user and outcome data associated with the exercise, wherein the exercise apparatus comprises at least one pedal that is electronically adjustable to provide at least a minimum desired range of motion;
generate, based on the first user data, initial target data wherein the initial target data is associated with at least one of the first user, the exercise apparatus, and the exercise, wherein the initial target data comprises at least the at least minimum desired range of motion;
receive measurement data associated with at least one of the first user, the exercise apparatus, and the exercise, wherein the measurement data is associated with one or more sensors;
determine differential data, wherein the determining is based on one or more differences between the initial target data and the measurement data; and
generate, via an artificial intelligence engine and based on the differential data, a machine learning model trained to generate optimized target data.
17 . The system of claim 16 , wherein the memory further causes the processing device to control, based on the optimized target data, the exercise apparatus, and transmit to an interface of the exercise apparatus, a message based on at least one of the differential data and the optimized target data.
18 . The system of claim 17 , wherein the memory further causes the processing device to receive, from at least one of first and second user sensors, user sensor data associated with an attribute of the user, wherein the measurement data comprises the user sensor data.
19 . The system of claim 18 , wherein the memory further causes the processing device to communicate to the first user, with an interface of the exercise apparatus, a message to adjust a position of one or more of the first and second user sensors, wherein the message is based on at least one of the differential data, the initial target data, the user sensor data, and the optimized target data.
20 . The system of claim 16 , wherein the differential data is further based on one or more differences between the measurement data and statistical data, wherein the statistical data is associated with at least one of the first user, a second user, the exercise apparatus, and the exercise.