Abstract: A computer-implemented method for facilitating cardiac rehabilitation among eligible users is disclosed. The method includes the steps of (1) receiving health information associated with a user; (2) determining, based on the health information, whether the user is eligible for cardiac rehabilitation, wherein the cardiac rehabilitation uses an electromechanical machine; and (3) in response to determining the user is eligible for cardiac rehabilitation, generating a treatment plan for the user, wherein the treatment plan pertains to a cardiac rehabilitation that is specific to the user, and the treatment plan comprises use of the electromechanical machine.

Abstract: A computer-implemented method for (1) receiving one or more characteristics of a user, wherein the one or more characteristics comprise personal information, performance information, measurement information, or some combination thereof, (2) generating, using one or more trained machine learning models, a treatment plan for the user, wherein the treatment plan is generated based on the one or more characteristics of the user, and the treatment plan comprises: (i) a dietary plan that is tailored to manage one or more medical conditions associated with the user, and (ii) an exercise plan comprises one or more exercises associated with the one or more medical conditions, and (3) presenting, via a display device, at least a portion of the treatment plan comprising the dietary plan.

Abstract: The present disclosure provides a method comprising the steps: receiving first patient data, wherein the first patient data includes at least a first patient identifier associated with the first patient and a first treatment plan including an exercise; receiving first measurement data, where the first measurement data is associated with a first performance data; receiving second measurement data, where the second measurement data is associated with at least one of a second performance data and a second patient identifier; one of anonymiztion of and pseudonymization of the second patient identifier; determining, via an artificial intelligence engine and based on one or more differences between the first and the second measurement data, differential data; and presenting, with a patient interface, at least one of the differential data, first measurement data, second measurement data, and the first patient data.

Abstract: A method is disclosed for generating an improved exercise plan for a user to perform using at least an exercise machine. The method includes receiving data pertaining to the user. The data includes a physical activity goal the user desires to achieve and the physical activity goal includes levels of attainment to achieve. The method includes generating, by an artificial intelligence engine, the improved exercise plan. The improved exercise plan includes a set of exercises to be performed by the user to achieve the levels of attainment associated with the physical activity goal. The artificial intelligence engine uses at least one data source configured to include information pertaining to one or more exercises and at least one of the levels of attainment associated with the physical activity goal. The method includes transmitting the improved exercise plan to a computing device.

Abstract: Methods, systems, and computer-readable mediums for generating, by an artificial intelligence engine, an alignment plan capable of enabling, during a treatment session, the aligning of a user's body. The method comprises generating machine learning models trained to identify alignment plans. The method also comprises receiving treatment data that comprises a first position of the user's body, aspects of a treatment plan, and one or more attributes of the user. The method further comprises generating the alignment plan by using the machine learning models. The generating is based on at least the aspects of the treatment plan and at least one of the one or more attributes of the user. The alignment plan may comprise a target position of the user's body and one or more elements for adjusting the user's body from the first position to the target position. The method also comprises transmitting the plan to a computing device.

Abstract: Systems, methods, and computer-readable media are disclosed relating to the control of electromechanical devices. An artificial intelligence (AI) engine may be trained with machine learning techniques on data that includes one or more operational parameters of a treatment apparatus that includes one or more electromechanical elements. Based on user data, some of which may be from the treatment apparatus, the AI engine may predict a probability that a user may not have a successful rehabilitation treatment. A modified treatment plan may be generated, and an operational parameter of the treatment apparatus may be controlled e.g. by using computer instructions to cause the treatment apparatus to operate in a certain manner for further rehabilitation treatment. Thus, real-world physical effects on a treatment apparatus may be due to using AI techniques that determine particular modifications to a treatment plan, which may also increase success chances for user rehabilitation in a therapeutic environment.

Abstract: Systems, methods, and computer-readable mediums for operating an electromechanical device are disclosed. The system includes, in one example, the electromechanical device, a patient portal, and a computing device. The computing device is configured to receive user data relating to a user, and receive treatment data relating to treatment plans and outcomes. The computing device is also configured to generate a prehabilitation plan by using a machine learning model to process the user data and the treatment data. The computing device is further configured to select, for the electromechanical device, an electromechanical device configuration that enables exercises of the prehabilitation plan to be performed by the user such that performance improves an area of the user’s body. The computing device is also configured to enable the electromechanical device to implement the electromechanical device configuration.

Abstract: A system and method for improving completion of an exercise is disclosed herein. In one embodiment, a method of a control system to improve compliance with an exercise plan for an exercise machine includes receiving one or more load measurements from one or more load cells of the exercise machine. The method also includes comparing the one or more load measurements to one or more target thresholds, and determining whether the one or more load measurements exceed the one or more target thresholds. Responsive to determining that the one or more load measurements exceed the one or more target thresholds, the method also includes causing a user interface to present an indication that the one or more target thresholds have been exceeded and an exercise is complete, wherein the exercise is included in the exercise plan.

Abstract: A method for updating a treatment plan. The treatment plan is associated with a user using a treatment apparatus to perform the treatment plan. The method includes receiving first data associated with a first diagnosis of the user. The method includes generating, based on the first data, an initial treatment plan to be performed on the treatment apparatus by the user. The method includes receiving second data associated with a first attribute of the user. The method includes generating, via an artificial intelligence engine, a machine learning model trained to generate an updated treatment plan based on the initial treatment plan and the second data.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A computer-implemented system may include a rowing machine, an interface comprising a display configured to present information pertaining to the treatment plan, and at least one processing device configured to receive, from sensors, measurements associated with the user, wherein the measurements are received while the user performs the treatment plan; determine, via machine learning models, content items to present to the user, wherein the determining is based on the measurements and one or more characteristics of the user; and while the user performs the treatment plan using the rowing machine, cause presentation of the content items on the interface, wherein the content items comprise at least information related to a state of the user, and the state of the user is associated with the measurements, the characteristics, or some combination thereof.