Rom Technologies, Inc.

Joel Rosenberg

Steven Mason

Jay L Waddell

Systems, methods, and computer-readable media for assigning remote monitoring sessions. The system includes 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 performing a treatment plan. The one or more sensors are configured to determine measurements of one or more vital signs associated with the user. The one or more processing devices are configured to receive a request to conduct a monitored session of the user performing the treatment plan. The one or more processing devices are also configured to determine whether conducting the monitored session with a computing device will violate one or more remote monitoring rules. The one or more processing device are further configured to initiate the monitored session by displaying the measurements on a display of the computing device while the user performs the treatment plan.

Systems and methods for assigning healthcare professionals to remotely monitor users performing treatment plans on electromechanical machines

A computer-implemented system includes one or more processing devices configured to receive user information associated with a user, generate a selected set of the user information, determine, based on the selected set of the user information, at least one of a first probability of surviving one or more procedures and a second probability indicating an improvement, resulting from the one or more procedures, in quality-of-life metrics for the user, generate, based on the at least one of the first probability and the second probability and on the selected set of the user information, one or more recommendations of whether the user should undergo the one or more procedures, and generate, based on the one or more recommendations, a treatment plan that includes one or more exercises directed to modifying the at least one of the first probability and the second probability.

System and method for determining, based on advanced metrics of actual performance of an electromechanical machine, medical procedure eligibility in order to ascertain survivability rates and measures of quality-of-life criteria

A computer-implemented method is disclosed. The method includes receiving, from one or more sensors, one or more measurements associated with a user, where the one or more measurements are received while the user performs a treatment plan, and an electromechanical machine is configured to be manipulated by the user while the user is performing the treatment plan. The method also includes determining, via one or more machine learning models, one or more content items to present to the user, where the determining is based on the one or more measurements and one or more characteristics of the user. A presentation of the one or more content items is provided on an interface while the user performs the treatment plan using the electromechanical machine.

System and method for enabling residentially-based cardiac rehabilitation by using an electromechanical machine and educational content to mitigate risk factors and optimize user behavior

In one embodiment, a computer-implemented system includes an electromechanical machine configured to be manipulated by a user while the user performs a treatment plan. The treatment plan includes a high-intensity interval training (HIIT) session. A processing device is configured to initiate, using the electromechanical machine, the HIIT session, receive, via one or more sensors, one or more measurements pertaining to the electromechanical machine, determine whether the one or more measurements exceed one or more of one or more corresponding thresholds, and in response to determining that the one or more measurements exceed one or more of the one or more corresponding thresholds, modify the treatment plan to cause operation of the electromechanical machine to be modified.

Systems and methods for using artificial intelligence and machine learning to generate treatment plans having dynamically tailored cardiac protocols for users to manage a state of an electromechanical machine

A computer-implemented system includes processing devices configured to receive attribute data associated with a user, determine, based on the attribute data, at least one of a probability of being eligible for the first bariatric procedure to be performed on the user, and generate, based on the at least one probability, a treatment plan that includes one or more exercises directed to modifying the at least one of the first probability and the second probability, and a treatment apparatus configured for implementation of the treatment plan.

System and method for using AI ML and telemedicine to perform bariatric rehabilitation via an electromechanical machine

A computer-implemented system includes a processing device configured to receive a plurality of user and blood vessel characteristics associated with a user, generate a selected set of user and blood vessel characteristics, determine, based on the selected set of the user and blood vessel characteristics, a probability that angiogenesis will occur, and generate, based on the probability and the selected set of the user and blood vessel characteristics, a treatment plan that includes one or more exercises directed to modifying the probability that angiogenesis will occur, and a treatment apparatus configured to implement the treatment plan while the treatment apparatus is being manipulated by the user.

System and method for using AI/ML to generate treatment plans to stimulate preferred angiogenesis

A computer-implemented method including receiving, at a computing device, a first treatment plan designed to treat a cardiovascular health issue of a user. The first treatment plan comprises at least two exercise sessions that, based on the cardiovascular health issue, enable the user to perform an exercise at different exertion levels. While the user uses a treatment apparatus to perform the first treatment plan for the user, the computing device receives cardiovascular data from one or more sensors configured to measure the cardiovascular data associated with the user, and transmits the cardiovascular data. Wherein a machine learning model is used to generate a second treatment plan. The second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the cardiovascular data, and the cardiovascular health issue of the user. The method includes receiving the second treatment plan.

Systems and methods for using artificial intelligence to implement a cardio protocol via a relay-based system

A computer-implemented system includes an interface comprising a display configured to display interface presentation data pertaining to a plurality of users each using a respective electromechanical machine to perform a treatment plan and one or more processing devices configured to receive selected measurement information associated with each of the plurality of users, generate the interface presentation data based on the selected measurement information; and enable the interface to present, in respective portions of the display, the interface presentation data for each of the plurality of users. Each of the respective portions comprises a graphical element that presents, in real-time or near real-time, at least one type of the selected measurement information.

System and method for an enhanced healthcare professional user interface displaying measurement information for a plurality of users

A computer-implemented system includes a treatment apparatus configured to implement a treatment plan while the treatment apparatus is being manipulated by a user and a processing device configured to receive a plurality of risk factors, each of the plurality of risk factors being associated with one or more medical events or outcomes for a user; generate a set of the risk factors; determine, based on the set of the risk factors and measurement information associated with the user, one or more individual probabilities that the one or more medical events or outcomes will occur while the treatment apparatus is being manipulated by the user; and perform, based on the one or more individual probabilities, one or more corrective actions associated with the one or more medical events or outcomes.

Systems and methods for using artificial intelligence and machine learning to predict a probability of an undesired medical event occurring during a treatment plan

Systems including an elliptical machine and a processing device. The processing device may be configured to receive, before or while a user operates the elliptical machine, one or more messages pertaining to the user or a use of the elliptical machine by the user. The processing device may be also configured to determine whether the one or more messages were received by the processing device. In response to determining that the one or more messages were not received by the processing device, the processing device may be configured to determine, via one or more machine learning models, one or more actions to perform. The one or more actions may include at least one of initiating a telecommunications transmission, stopping operation of the elliptical machine, and modifying one or more parameters associated with the operation of the elliptical machine.

United states systems and methods for using elliptical machine to perform cardiovascular rehabilitation

A computer-implemented system includes one or more processing devices configured to receive attribute data associated with a user, generate, based on the attribute data and a first health issue of the user, a selected set of the attribute data, the first health issue corresponding to a first long-term care health issue of the user, determine, based on the selected set of the attribute data, a first probability associated with treating the first health issue, generate, based on the first probability, a treatment plan that includes one or more exercises directed to modifying the first probability, and modify, based on at least one of the first probability and a second probability, the treatment plan. The second probability is associated with treating a second health issue of the user. A treatment apparatus is configured to implement the treatment plan while the treatment apparatus is being manipulated by the user.

System and method for using AI, machine learning and telemedicine for long-term care via an electromechanical machine

A computer-implemented system includes one or more processing devices configured to receive attribute data associated with a user, generate, based on a cardio-oncologic condition of the user, a selected set of the attribute data, determine, based on the selected set of the attribute data, a first probability of improving a cardio-oncologic condition of the user subsequent to at least one of a cardio-oncologic procedure and a cardio-oncologic treatment being performed on the user, and generate, based on the first probability, a treatment plan that includes one or more exercises directed to modifying the first probability, and a treatment apparatus configured for implementation of the treatment plan.

System and method for using AI ML and telemedicine for cardio-oncologic rehabilitation via an electromechanical machine

Systems and methods for using elliptical machine to perform cardiovascular rehabilitation

A system includes a treatment apparatus configured to implement a treatment plan for rehabilitation to be performed by a user and a processing device configured to receive attribute data associated with the user; generate, based on the rehabilitation, selected attribute data; determine, based on the selected attribute data, the rehabilitation, and a rehabilitation goal associated with the rehabilitation, one or more probabilities of attaining the rehabilitation goal within respective one or more numbers of rehabilitation sessions to be performed by the user using the treatment apparatus; provide, based on the one or more probabilities, an indication of the one or more numbers of rehabilitation sessions; and generate, based on a selected number of rehabilitation sessions from among the one or more numbers of rehabilitation sessions, the treatment plan. The treatment plan includes one or more exercises directed to attaining the rehabilitation goal within the selected number of rehabilitation sessions.

Systems and methods for using AI ML to predict, based on data analytics or big data, an optimal number or range of rehabilitation sessions for a user

A computer-implemented method is disclosed. The method includes determining a maximum target heart rate for a user using an electromechanical machine to perform a treatment plan, wherein the electromechanical machine is configured to be manipulated by the user while the user is performing the treatment plan. The method includes receiving, via an interface, an input pertaining to a perceived exertion level of the user, wherein the interface comprises a display configured to present information pertaining to the treatment plan. Based on the perceived exertion level and the maximum target heart rate, the method includes determining an amount of resistance for the electromechanical machine to provide via one or more pedals physically or communicatively coupled to the electromechanical machine. While the user performs the treatment plan, the method includes causing the electromechanical machine to provide the amount of resistance.

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Washington, DC, United States of America

Joel Rosenberg