ROM Technologies, Inc.

A computer-implemented method including receiving, at a computing device, a first treatment plan designed to treat a sexual performance health issue of a user. The first treatment plan comprises at least two exercise sessions that, based on the sexual performance 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. A machine learning model is used to generate a second treatment plan that 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.

A method is disclosed for using an artificial intelligence engine to onboard a user for an exercise plan. The method includes generating a machine learning model trained to receive as input onboarding data associated with a user and an onboarding protocol and, based on the onboarding data and the onboarding protocol, output an exercise plan. While a user performs an exercise using the exercise device, the method includes receiving the onboarding data associated with the user. The method includes determining, by the machine learning model using the onboarding data and the onboarding protocol, a fitness level of the user. The onboarding protocol includes exercises with tiered difficulty levels, the onboarding protocol increases a difficulty level for a subsequent exercise when an exercise is completed, and based on a completion state of a last exercise performed, the fitness level is determined. The method includes selecting a difficulty level for each exercise.

A computer-implemented system includes an interface comprising a display configured to display interface presentation data pertaining to a user using an electromechanical machine to perform a treatment plan and one or more processing devices configured to receive selected measurement information associated with the user and treatment plan information associated with the treatment plan, generate the interface presentation data based on the selected measurement information and the treatment plan information, and enable the interface to present, in a portion of the display, the interface presentation data for the user. The portion of the display includes one or more graphical elements that present, in real-time or near real-time, at least one type of the selected measurement information and the treatment plan information.

A computer-implemented method is disclosed. The method includes determining whether one or more messages have been received while a user performs a treatment plan. The one or more messages may pertain to at least one of a user and a usage of the electromechanical machine by the user. The electromechanical machine may be configured to be manipulated by the user while the user is performing a treatment plan. The method also includes, responsive to determining that the one or more messages have not been received, determining, using one or more machine learning models, one or more preventative actions to perform. The method also includes causing the one or more preventative actions to be performed.

Disclosed are systems and methods for a computerized framework that leverages artificial intelligence (AI)/machine learning (ML) mechanisms to assign selected individuals to military operations. The disclosed framework comparatively analyzes an ops sheet of a military operation and profile data related to a user(s), and automatically determines user(s) who are optimal for the operation. The determined user or users possess the physical and/or intellectual capabilities to accurately and efficiently, with respect to real-world and electronic resources, perform and complete the operation. The disclosed framework provides a computerized platform that selects users for highly specific tasks based on the users' analyzed skill sets, and based on computerized determinations of how such users are predicted to perform using those skill sets, securely and/or confidentially provides the users access to information related to the operation.

A computer-implemented method is disclosed. The method includes receiving, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user. The first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue, enable the user to perform an exercise at different exertion levels. Next, while the user uses the electromechanical machine to perform the first treatment plan, receiving, at the computing device, data from sensors configured to measure the data associated with the invasive surgical-related health issue and transmitting the data. One or more machine learning models are 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 data, and the invasive surgical-related health issue. The method additionally includes receiving the second treatment plan.

Systems, methods, and computer-readable media for and improvement rehabilitation infrastructure. For example, a method may include receiving, from one or more of an electromechanical machine, a sensor, and a computing device, data associated with a user that uses the electromechanical machine to perform a treatment plan. The method may include, based on one or more correlations of one or more indicators included in the data, generating, using an artificial intelligence engine, a unique data signature associated with a life-threatening event of the user. The unique data signature is generated when the one or more indicators satisfy a respective urgent threshold level, and the one or more indicators include a pain measurement and a measurement of revolutions per minute. Based on the unique data signature associated with the one or more life-threatening events, the method may include performing one or more preventative actions.

A method includes receiving user data for a user capable of operating an electromechanical device. The user data comprises health history data related to one or more health indicators of the user. The method further includes generating a health improvement plan by using a machine learning model to process the user data. The health improvement plan includes an exercise session to be performed on the electromechanical device. The method further includes providing the health improvement plan to one or more user portals. The method further includes selecting, for the electromechanical device, a device configuration that corresponds to the health improvement plan. The device configuration includes mode data related to one or more modes the electromechanical device is capable of operating during the exercise session. The method further includes providing the device configuration to the electromechanical device such that the device configuration may be implemented on the electromechanical device.

Systems, methods, and computer-readable media for and improvement rehabilitation infrastructure. For example, a method may include using, inter alia, objective data to optimize treatment plans to support user progression during rehabilitation for the purpose of assisting in determining AI-driven interventions by using machine learning to generate at least one data signature. In some embodiments, a method may perform AI-driven interventions via optimization of treatment plans in order to support user progression during rehabilitation by performing, based on data signatures related to medically-associated events, selective data injection. Some other embodiments use evidence-based research to enable AI and machine learning to select and modify treatment plans in real-time at scale. Further, the improved rehabilitation infrastructure enables, via pain management, user compliance with treatment plans.

A computer-implemented system includes an assistant interface for providing remote medical assistance to aid a patient in performing various aspects of a rehabilitation regimen for a body part comprising a joint, a bone, or a muscle group. The assistant interface is configured to communicate, via a network connection, a telemedicine signal with the patient interface. The telemedicine signal is configured to control the patient interface and/or a treatment apparatus configured to be manipulated by the patient to perform the rehabilitation regimen. The patient interface and the treatment apparatus are at a patient location geographically separate from a location of the assistant interface. The telemedicine signal includes one or more of an audio signal, an audiovisual signal, an interface control signal for controlling a function of the patient interface, and/or an apparatus control signal for changing an operating parameter of the treatment apparatus.