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 patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session. The computer-implemented system may also include 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; write to an associated memory, for access by an artificial intelligence engine, the treatment data; receive, from the artificial intelligence engine, at least one prediction; identify a threshold corresponding to the at least one prediction; and, in response to a determination that the at least one prediction is outside of the range of the threshold, update the treatment data pertaining to the user to indicate the at least one prediction.

Abstract: A computer-implemented system may include an electromechanical machine configured to be manipulated by a user while the user performs a treatment plan, an interface comprising a display configured to present information associated with the treatment plan, and a processing device configured to receive, from one or more data sources, information associated with the user, wherein the information comprises one or more risk factors associated with a cardiac condition or a cardiac outcome, generate, using one or more trained machine learning models, the treatment plan for the user, wherein the treatment plan is generated based on the information associated with the user, and the treatment plan comprises one or more exercises associated with managing the one or more risk factors in order to reduce a probability of a cardiac intervention for the user, and transmit the treatment plan to cause the electromechanical machine to implement the one or more exercises.

Abstract: Methods, systems, and computer-readable mediums for generating, by an artificial intelligence engine, treatment plans for optimizing a user outcome. The method comprises receiving attribute data associated with a user. The attribute data comprises one or more symptoms associated with the user. The method also comprises, while the user uses a treatment apparatus to perform a first treatment plan for the user, receiving measurement data associated with the user. The method further comprises generating, by the artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user. The generating is based on at least the attribute data associated with the user and the measurement data associated with the user. The second treatment plan comprises a description of one or more predicted disease states of the user. The method also comprises transmitting, to a computing device, the second treatment plan for the user.

Abstract: 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.

Abstract: A computer-implemented system includes an electromechanical machine and a processing device communicatively coupled to motors. The processing device executes instructions to receive first data comprising information pertaining to motion of one or more pedals of the electromechanical machine, wherein the motion is associated with one or more users performing one or more treatment plans; receive second data comprising information pertaining to one or more characteristics of the one or more users performing the treatment plan, wherein the characteristics comprise performance information, measurement information, personal information, or some combination thereof; identify one or more correlations between at least some of the first data and at least some of the second data; and generate, based on the one or more treatment plans and the one or more correlations, one or more motion profiles for an assembly of the electromechanical machine.

Abstract: A machine includes a body with an arm having a proximal end and a distal end. The proximal end is pivotally mounted to the body, and the distal end is free to move relative to the body. A carriage is slidably mounted to the arm for movement along a length of the arm. A pedal assembly is rotatably coupled to the carriage for movement with the carriage along the length of the arm. The pedal assembly supports a foot of a user of the machine. Motors are mounted to the body and spaced apart from each other and spaced apart from the distal end of the arm. Cables are coupled to respective ones of the motors and to the carriage. A control system selectively extends and retracts the cables with the motors to cause the pedal assembly to move in prescribed pedaling patterns relative to the body.

Abstract: A computer-implemented system includes one or more processing devices configured to receive attribute data associated with a user, generate, based on a pulmonary 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 pulmonary condition of the user subsequent to at least one of a pulmonary procedure being performed on the user, a pulmonary treatment being performed on the user, and a pulmonary diagnosis, and generate, based on the first probability, a treatment plan that includes one or more exercises directed to modifying the first probability. A treatment apparatus is configured to enable implementation of the treatment plan.

Abstract: A treadmill comprises a pair of rollers and a belt coupled to and extending between the pair of rollers. The belt encircles a support platform suspended between the pair of rollers. A motor is operatively coupled to one of the pair of rollers to rotate the one of the pair of rollers such that rotation of the rollers causes the belt to rotate around the support platform suspended between the pair of rollers. The treadmill further comprises a processing device configured to cause the treadmill to perform an action associated with a use of the treadmill by the user based on whether the processing device receives certain messages; cause content items to be presented to the user; assign the treatment plan to the treadmill based on a determined eligibility of the user; and implement the treatment plan to reduce a probability of a cardiac intervention for the user.

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 including one or more prescribed exercises for a user to perform using the electromechanical machine; generate, based on the data, a motion profile for an assembly of the electromechanical machine, wherein the assembly is coupled to a carriage to enable movement along a length of an arm, wherein the carriage is coupled to each cable of the first set of cables; execute a transformation function to implement a desired virtual apparatus model using the electromechanical machine, 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 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: 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 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: 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 one or more users; (2) for each user of the one or more users: determining, based on health information associated with the user, a respective eligibility of the user for cardiac rehabilitation; (3) determining, based on the respective eligibilities, that at least one user of the one or more users is eligible for cardiac rehabilitation; (4) generating a treatment plan for the at least one user, where the treatment plan pertains to a cardiac rehabilitation that is specific to the at least one user; and (5) assigning the treatment plan to at least one electromechanical machine to enable the user to perform the cardiac rehabilitation.

Abstract: In one embodiment, a method is disclosed. The method includes, while the patient uses the treatment apparatus, controlling, based on a treatment plan for a patient, a treatment apparatus. The method includes receiving, by a processing device, data from an electronic device, wherein the data comprises one of a position of a body part of the patient or a force exerted by the body part. The method includes storing, via the processing device, the data for the patient in a computer-readable medium. The method includes causing, via a processing device, presentation of a user interface on a patient interface. The user interface comprises an adjustment confirmation control, and the adjustment confirmation control is configured to solicit a response regarding the patient's comfort level with the one of the position of the body part or the force exerted by the body part.

Abstract: 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.

Abstract: Computer-implemented systems, computer-implemented methods, and tangible, non-transitory computer-readable media for detecting abnormal heart rhythms of a user performing treatment plan with an electromechanical machine. The system includes, in one embodiment, 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 the user is performing a treatment plan. The one or more sensors are configured to determine one or more measurements associated with the user. The one or more processing devices are configured to receive, from the one or more sensors while the user performs the treatment plan, the one or more measurements associated with the user. The one or more processing devices also configured to determine, using one or more machine learning models, a probability that the one or more measurements indicate that the user satisfies a threshold for a condition associated with an abnormal heart rhythm.

Abstract: A smart exercise device includes an interface comprising a smart mirror configured to present information pertaining to a treatment plan being performed by a user while the user interacts with the smart exercise device. A processing device is configured to cause the smart exercise device to perform one or more functions related to the treatment plan, such as cause one or more preventative actions to be performed based on whether the processing device receives one or more messages, cause one or more content items to be presented to the user on the interface, assign the treatment plan to the smart exercise device based on a determined eligibility of the user, and implement the treatment plan to reduce a probability of a cardiac intervention for the user.

Abstract: A treadmill comprises a pair of rollers and a belt coupled to and extending between the pair of rollers. The belt encircles a support platform suspended between the pair of rollers. A motor is operatively coupled to one of the pair of rollers to rotate the one of the pair of rollers such that rotation of the rollers causes the belt to rotate around the support platform suspended between the pair of rollers. The treadmill further comprises a processing device configured to cause the treadmill to perform an action associated with a use of the treadmill by the user based on whether the processing device receives certain messages; cause content items to be presented to the user; assign the treatment plan to the treadmill based on a determined eligibility of the user; and implement the treatment plan to reduce a probability of a cardiac intervention for the user.

Abstract: The embodiments set forth a technique implemented by a computing device. The technique includes the steps of (1) receiving one or more characteristics associated with a user, wherein the one or more characteristics comprise personal information, performance information, measurement information, cohort information, familial information, comorbidity information, healthcare professional information, or some combination thereof; (2) determining, based on the one or more characteristics, one or more conditions of the user, wherein the one or more conditions pertain to cardiac health, pulmonary health, bariatric health, oncologic health, cardio-oncologic health, or some combination thereof; (3) based on the one or more conditions, identifying, using one or more trained machine learning models, one or more subgroups to present via the display, wherein the one or more subgroups represent different partitions of the one or more characteristics; and (4) presenting, via the display, the one or more subgroups.

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.