Systems and methods for using artificial intelligence and machine learning to detect abnormal heart rhythms of a user performing a treatment plan with an electromechanical machine
Computer-implemented systems, computer-implemented methods, and tangible, non-transitory computer-readable media for detecting abnormal heart rhythms of a user performing treatment plan with an electromechanical machine. The system includes, in one embodiment, an electromechanical machine, one or more sensors, and one or more processing devices. The electromechanical machine is configured to be manipulated by a user while the user is performing a treatment plan. The one or more sensors are configured to determine one or more measurements associated with the user. The one or more processing devices are configured to receive, from the one or more sensors while the user performs the treatment plan, the one or more measurements associated with the user. The one or more processing devices also configured to determine, using one or more machine learning models, a probability that the one or more measurements indicate that the user satisfies a threshold for a condition associated with an abnormal heart rhythm. The one or more processing devices are further configured to perform one or more preventative actions responsive to determining that the one or more measurements indicate the user satisfies the threshold for the condition associated with the abnormal heart rhythm. The one or more preventative actions are determined using the one or more machine learning models.
1. A computer-implemented system, comprising:
an electromechanical machine configured to be manipulated by a user while the user is performing a treatment plan;
one or more sensors configured to determine one or more measurements associated with the user; and
one or more processing devices configured to:
receive, from the one or more sensors while the user performs the treatment plan, the one or more measurements associated with the user,
determine, using one or more machine learning models, a probability that the one or more measurements indicate that the user satisfies a threshold for a condition associated with an abnormal heart rhythm, and
perform one or more preventative actions responsive to determining that the one or more measurements indicate the user satisfies the threshold for the condition associated with the abnormal heart rhythm, wherein the one or more preventative actions are determined using the one or more machine learning models, and wherein the one or more preventative actions comprise at least one preventative action selected from the group consisting of initiating a telecommunications transmission, stopping operation of the electromechanical machine, and modifying one or more parameters associated with the operation of the electromechanical machine.
2. The computer-implemented system of claim 1 , wherein, to perform the one or more preventative actions, the one or more processing devices are further configured to initiate a call to an emergency service provider.
3. The computer-implemented system of claim 1 , wherein, to perform the one or more preventative actions, the one or more processing devices are further configured to initiate a telemedicine session with a computing device associated with a healthcare professional.
4. The computer-implemented system of claim 1 , further comprising a display, wherein, to perform the one or more preventative actions, the one or more processing devices are further configured to present, on the display, one or more instructions to modify usage of the electromechanical machine.
5. The computer-implemented system of claim 1 , wherein the condition associated with the abnormal heart rhythm comprises at least one condition selected from the group consisting of atrial fibrillation, atrial flutter, supraventricular tachycardia, ventricular fibrillation, and ventricular tachycardia.
6. The computer-implemented system of claim 1 , wherein the one or more sensors comprise at least one sensor selected from the group consisting of a pulse oximeter, an electrocardiogram sensor, a heart rate sensor, a blood pressure sensor, a temperature sensor, a force sensor, and a continuous glucose monitor sensor.
7. The computer-implemented system of claim 1 , wherein the one or more machine learning models are trained to implement a photoplethysmography algorithm or an electrocardiogram algorithm.
8. A computer-implemented method comprising:
receiving, from one or more sensors while a user performs a treatment plan on an electromechanical machine, one or more measurements associated with the user;
determining, using one or more machine learning models, a probability that the one or more measurements indicate that the user satisfies a threshold for a condition associated with an abnormal heart rhythm; and
performing one or more preventative actions responsive to determining that the one or more measurements indicate the user satisfies the threshold for the condition associated with the abnormal heart rhythm, wherein the one or more preventative actions are determined using the one or more machine learning models, and wherein the one or more preventative actions comprise at least one preventative action selected from the group consisting of initiating a telecommunications transmission, stopping operation of the electromechanical machine, and modifying one or more parameters associated with the operation of the electromechanical machine.
9. The computer-implemented method of claim 8 , wherein performing the one or more preventative actions comprising initiating the telecommunications transmission comprises initiating a call to an emergency service provider.
10. The computer-implemented method of claim 8 , wherein performing the one or more preventative actions comprising initiating a telemedicine session with a computing device associated with a healthcare professional.
11. The computer-implemented method of claim 8 , wherein performing the one or more preventative actions comprising presenting, on a display, one or more instructions to modify usage of the electromechanical machine.
12. The computer-implemented method of claim 8 , wherein the condition associated with the abnormal heart rhythm comprises at least one condition selected from the group consisting of atrial fibrillation, atrial flutter, supraventricular tachycardia, ventricular fibrillation, and ventricular tachycardia.
13. The computer-implemented method of claim 8 , wherein the one or more sensors comprise at least one sensor selected from the group consisting of a pulse oximeter, an electrocardiogram sensor, a heart rate sensor, a blood pressure sensor, a temperature sensor, a force sensor, and a continuous glucose monitor sensor.
14. The computer-implemented method of claim 8 , wherein the one or more machine learning models are trained to implement a photoplethysmography algorithm or an electrocardiogram algorithm.
15. One or more tangible, non-transitory computer-readable media storing instructions that, when executed, cause one or more processing devices to:
receive, from one or more sensors while a user performs a treatment plan on an electromechanical machine, one or more measurements associated with the user,
determine, using one or more machine learning models, a probability that the one or more measurements indicate that the user satisfies a threshold for a condition associated with an abnormal heart rhythm, and
perform one or more preventative actions responsive to determining that the one or more measurements indicate the user satisfies the threshold for the condition associated with the abnormal heart rhythm, wherein the one or more preventative actions are determined using the one or more machine learning models, and wherein the one or more preventative actions comprise at least one preventative action selected from the group consisting of initiating a telecommunications transmission, stopping operation of the electromechanical machine, and modifying one or more parameters associated with the operation of the electromechanical machine.
16. The one or more computer-readable media of claim 15 , wherein, to perform the one or more preventative actions, the instructions further cause the one or more processing devices to initiate a call to an emergency service provider or a telemedicine session with a computing device associated with a healthcare professional.
17. The one or more computer-readable media of claim 15 , wherein, to perform the one or more preventative actions, the instructions further cause the one or more processing devices to present, on a display, one or more instructions to modify usage of the electromechanical machine.
18. The one or more computer-readable media of claim 15 , wherein the condition associated with the abnormal heart rhythm comprises at least one condition selected from the group consisting of atrial fibrillation, atrial flutter, supraventricular tachycardia, ventricular fibrillation, and ventricular tachycardia.
19. The one or more computer-readable media of claim 15 , wherein the one or more sensors comprise at least one sensor selected from the group consisting of a pulse oximeter, an electrocardiogram sensor, a heart rate sensor, a blood pressure sensor, a temperature sensor, a force sensor, and a continuous glucose monitor sensor.
20. The one or more computer-readable media of claim 15 , wherein the one or more machine learning models are trained to implement a photoplethysmography algorithm or an electrocardiogram algorithm.