A computer-implemented system may include a treatment device configured to be manipulated by a user while the user is performing a treatment plan, a patient interface. and a computing device configured to: receive treatment data pertaining to the user who uses the treatment device to perform the treatment plan; identify at least one enhanced component using the treatment data; generate an enhanced environment using the at least one enhanced component and the treatment plan; output at least one aspect of the enhanced environment to at least one of the patient interface and another interface; receive subsequent treatment data pertaining to the user; and selectively modify at least one of the enhanced environment and at least one of the at least one aspect of the treatment plan and any other aspect of the treatment plan using the subsequent treatment data.

A method is disclosed for using an artificial intelligence engine to interact with a user of an exercise device during an exercise session. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive data as input, and based on the data, providing an output. While a user performs an exercise using the exercise device, the method includes receiving the data from an input peripheral of a computing device associated with the user. Based on the data being received from the input peripheral, the method includes determining, via the machine learning model, the output to control an aspect of the exercise device.

A method is disclosed for using an artificial intelligence engine to perform a control action. The control action is based on one or more measurements from a wearable device. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive the one or more measurements as input, and outputting, based on the one or more measurements, a control instruction that causes the control action to be performed. The method includes receiving the one or more measurements from the wearable device being worn by a user, determining whether the one or more measurements indicate, during an interval training session, that one or more characteristics of the user are within a desired target zone, and responsive to determining that the one or more measurements indicate the one or more characteristics of the user are not within the desired target zone during the interval training session, performing the control action.

A method is disclosed for using an artificial intelligence engine to modify resistance of one or more pedals of an exercise device. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive one or more measurements as input, and outputting, based on the one or more measurements, a control instruction that causes the exercise device to modify the resistance of the one or more pedals. The method includes receiving the one or more measurements from a sensor associated with the one or more pedals of the exercise device, determining whether the one or more measurements satisfy a trigger condition, and responsive to determining that the one or more measurements satisfy the trigger condition, transmitting the control instruction to the exercise device.

A treadmill includes a deck having a continuous track, and a plurality of slats fixedly connected to the track. The treadmill also includes a first post extending from the deck, a second post extending from the deck opposite the first post, and a first arm supported by the first post and including a first rotary control. The treadmill further includes a second arm opposite the first arm and supported by the second post. The second arm includes a second rotary control separate from the first rotary control. The first rotary control is configured to control a first function of the treadmill and the second rotary control is configured to control a second function of the treadmill different from the first function.

A computer-implemented system comprising a treatment apparatus, a patient interface, and a processing device is disclosed. The processing device is configured to receive treatment data pertaining to the user during the telemedicine session, wherein the treatment data comprises one or more characteristics of the user; determine, via one or more trained machine learning models, at least one respective measure of benefit one or more exercise regimens provide the user, wherein the determining the respective measure of benefit is based on the treatment data; determine, via the one or more trained machine learning models, one or more probabilities of the user complying with the one or more exercise regimens; and transmit the treatment plan to a computing device, wherein the treatment plan is generated based on the one or more probabilities and the respective measure of benefit the one or more exercise regimens provide the user.

In one embodiment, a computer-implemented system includes a treatment apparatus configured to be manipulated by a user while performing an exercise session, patient interfaces associated with users, and a server computing device configured to receive treatment data pertaining to the user, determine whether at least one characteristic of the user matches at least one second characteristic of a second user, where the second user belonging to a cohort. Responsive to determining the at least one characteristic of the user matches the at least one second characteristic of the second user, the server computing device is configured to assign the user to the cohort and select, via a trained machine learning model, a treatment plan for the user. Responsive to transmitting a signal to the patient interfaces of users in the cohort, the server computing device enables the patient interfaces to establish the virtual shared session between the patient interfaces.

A system that comprises a memory device storing instructions, and a processing device communicatively coupled to the memory device. The processing device executes the instructions to: receive user data obtained from records associated with a user; generate a modified treatment plan based on the user data; and send, to a treatment apparatus accessible to the user, the modified treatment plan, wherein the modified treatment plan causes the treatment apparatus to update at least one operational aspect of the treatment apparatus, and update at least one operational aspect of at least one other device communicatively coupled to the treatment apparatus.

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 determining whether one or more messages have been received from at least one of an electromechanical machine, a sensor, and an interface. Then 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.