US 11,923,065 B2
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
Joel Rosenberg, Brookfield, CT (US); and Steven Mason, Las Vegas, NV (US)
Assigned to ROM Technologies, Inc., Brookfield, CT (US)
Filed by ROM Technologies, Inc., Brookfield, CT (US)
Filed on Jun. 30, 2023, as Appl. No. 18/217,235.
Application 18/217,235 is a continuation in part of application No. 17/736,891, filed on May 4, 2022.
Application 17/736,891 is a continuation in part of application No. 17/379,542, filed on Jul. 19, 2021, granted, now 11,328,807, issued on May 10, 2022.
Application 17/379,542 is a continuation of application No. 17/146,705, filed on Jan. 12, 2021.
Application 17/146,705 is a continuation in part of application No. 17/021,895, filed on Sep. 15, 2020, granted, now 11,071,597, issued on Jul. 27, 2021.
Claims priority of provisional application 63/407,049, filed on Sep. 15, 2022.
Claims priority of provisional application 63/113,484, filed on Nov. 13, 2020.
Claims priority of provisional application 62/910,232, filed on Oct. 3, 2019.
Prior Publication US 2023/0360765 A1, Nov. 9, 2023
Int. Cl. A63B 24/00 (2006.01); G16H 20/30 (2018.01); G16H 50/30 (2018.01)
CPC G16H 20/30 (2018.01) [A63B 24/0062 (2013.01); G16H 50/30 (2018.01); A63B 2024/0065 (2013.01)] 20 Claims
OG exemplary drawing
 
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.