Abstract: 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.

Abstract: In one embodiment, a computer-implemented system includes treatment apparatuses configured to be manipulated by patients while performing an exercise session, patient interfaces associated with the plurality of patients, and a server computing device configured to receive first characteristics pertaining to the patients, and initiate a virtual shared session on the patient interfaces associated with the patients. The virtual shared session includes at least a set of multimedia feeds, and each multimedia feed of the set of multimedia feeds is associated with one or more of the patients. During the virtual shared session, the server computing device may present a first layout including the set of multimedia feeds, the first characteristics, or some combination thereof.

Abstract: 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.

Abstract: In some embodiments, a system for rehabilitation includes an electronic device including a memory device for storing instructions, a network interface card, and a sensor. The system includes a processing device operatively coupled to the memory device, the network interface card, and the sensor, wherein the processing device executes the instructions to determine angles while a user is engaging one or more pedals of an electromechanical device, and transmit, via the network interface card, the angles to a computing device controlling the electromechanical device, and based on the angles satisfying a range of one or more respective motion threshold conditions, wherein, while the user operates at least one of the one or more pedals, the transmitting the angles to the computing device causes the computing device to increase a diameter of a range of motion of one of the one or more pedals.

Abstract: 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.

Abstract: 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.

Abstract: A system for processing medical claims, comprising a processor configured to receive device-generated information from a medical device. The device-generated information includes performance information. The processor generates a first biometric signature, and using the device-generated information, generates a second biometric signature, wherein the second biometric signature uses the performance information. Using the first and second biometric signatures, the processor generates a signature comparison. Using the signature comparison, the processor generates a signature indicator and transmits the signature indicator.

Abstract: 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.

Abstract: 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.

Abstract: A system for rehabilitation is disclosed. The system for rehabilitation includes one or more electronic devices comprising one or more memory devices storing instructions, one or more network interface cards, and one or more sensors, wherein the one or more electronic devices are coupled to a user. The system for rehabilitation further includes one or more processing devices operatively coupled to the one or more memory devices, the one or more network interface cards, and the one or more sensors. The one or more processing devices are configured to execute the instructions to receive information from the one or more sensors. The one or more processing devices are further configured to execute the instructions to transmit the information to a computing device controlling an electromechanical device, via the one or more network interface cards.

Abstract: 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.

Abstract: A computer-implemented system includes an electromechanical machine and a processing device communicatively coupled to motors. The processing device executes instructions to receive data comprising a treatment plan, wherein the treatment plan includes one or more prescribed exercises for a user to perform using the electromechanical machine; generate, based on the data and certain criteria, a motion profile for an assembly of the electromechanical machine, wherein the motion profile comprises a movement shape of one or more portions of the electromechanical machine; execute a transformation function to implement, using the electromechanical machine, a desired virtual apparatus model, wherein, to implement the desired virtual apparatus model, the transformation function maps the motion profile to one or more coordinates in a domain; and control, using the desired virtual apparatus model, the one or more motors of the electromechanical machine.

Abstract: A computer-implemented system may include a treatment device configured to be manipulated by a user while the user is performing a treatment plan, and a first computing device configured to: receive treatment data pertaining to the user while the user uses the treatment device to perform the treatment plan; identify at least one aspect of the treatment data pertaining to the user associated with a first treatment device mode of the treatment device; and, in response to a determination that the at least one aspect of the treatment data is correlated with at least one second condition of the user, generate second condition information indicating at least the second condition.

Abstract: 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.

Abstract: Systems, methods, and computer-readable mediums for modifying, by an artificial intelligence engine, a treatment plan for optimizing patient outcome and pain levels during treatment sessions. The system includes, in one implementation, a treatment apparatus, a patient interface, and a computing device. The treatment apparatus is configured to be manipulated by a patient while the patient performs the treatment sessions. The computing device is configured to receive the treatment plan for the patient and treatment data pertaining to the patient. The computing device is also configured to receive patient input from the patient interface correlating with the pain levels of the patient. The computing device is further configured to use the treatment plan, the treatment data, and the patient input to generate at least one threshold. Responsive to an occurrence of exceeding the at least one threshold, the computing device is configured to modify the treatment plan.

Abstract: A method includes receiving data pertaining to a user that uses a treatment apparatus to perform a treatment plan. The data includes characteristics of the user, the treatment plan, and a result of the treatment plan. The method includes assigning the user to a cohort representing people having similarities to the characteristics of the user. The method includes receiving second data pertaining to a second user, the second data comprises characteristics of the second user. The method includes determining whether at least some of the characteristics of the second user match with at least some of the characteristics of the user, assigning the second user to the first cohort, and selecting, via a trained machine learning model, the treatment plan for the second user, and controlling, based on the treatment plan, the treatment apparatus while the second user uses the treatment apparatus.

Abstract: A pedal assembly for electromechanical exercise or rehabilitation of a user is disclosed and can include pedals to engage appendages of a user. A spindle supports each pedal and has a spindle axis. A pedal arm assembly is located between the spindle and an axle. The pedal arm assembly is radially offset from the spindle axis to define a range of adjustability for the pedal relative to the axle. The pedal arm assembly can include an electrically-actuated coupling assembly to adjust the position of the pedal in response to a control signal, and regulate motion of the user engaged with the pedals.

Abstract: A pedal assembly for electromechanical exercise or rehabilitation of a user is disclosed and can include pedals to engage appendages of a user. A spindle supports each pedal and has a spindle axis. A pedal arm assembly is located between the spindle and a rotational axle of the equipment. The pedal arm assembly is radially offset from the spindle axis to define a range of radial adjustability for the pedal relative to the rotational axle. The pedal arm assembly can include an electrically-actuated coupling assembly to adjust the radial position of the pedal in response to a control signal, and regulate motion of the user engaged with the pedals.

Abstract: 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.

Abstract: 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.