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: 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 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: 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 system includes an electromechanical machine configured to be manipulated by a user while the user performs a treatment plan. The system also includes a computing device configured to: receive treatment data pertaining to the user who uses the electromechanical machine to perform the treatment plan; and modify at least one aspect of the treatment plan in response to receiving treatment plan input including at least one modification to the at least one of the at least one aspect and any other aspect of the treatment plan; and control, based on the modification, the operation of the electromechanical machine.

Abstract: A method is disclosed for providing, by an artificial intelligence engine, an improved treatment plan to use with an electromechanical machine. The method includes receiving, from a data source, clinical information pertaining to results of using the electromechanical machine to perform particular treatment plans for people having certain characteristics. The clinical information has a first data format. The method also includes translating a portion of the clinical information from the first data format to a medical description language used by the artificial intelligence engine, determining, based on the portion of the clinical information described by the medical description language and a plurality of characteristics pertaining to a patient, the improved treatment plan for the patient to follow using the electromechanical machine, and controlling, based on the improved treatment plan, the electromechanical machine.

Abstract: Systems and methods for identifying a condition of a user. An electromechanical machine is configured to be manipulated by the user for. An interface is communicably coupled to the electromechanical machine. A processing device and a memory are communicatively coupled to the processing device, and the memory includes computer readable instructions that, when executed by the processing device, cause the processing device to receive, from one or more sensors, one or more sensor inputs representative of one or more of characteristics of an anatomical structure of the user, calculate an infection probability based on the one or more characteristics of the anatomical structure, and output, to the interface, a representation of the infection probability.

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: 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 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 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 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 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 method for displaying clinician user interface generated by a computer is disclosed. The method may include, displaying clinician user interface generated by a computer. The method may also include a profile display presenting information regarding a treatment history of a patient. The method may also include a protocol management display presenting a treatment plan, with the treatment plan comprising a plurality of treatment protocols. The method may also include a plan modification control configured to modify the plurality of treatment protocols of the treatment plan. The method may include a login interface configured to enable a person to access the clinician user interface by providing a credential associated with one of a plurality of user accounts. The method may include wherein each of the plurality of user accounts has a corresponding set of permissions controlling access to patient data on the clinician user interface.

Abstract: The present disclosure provides a method for performing a treatment plan, wherein the method comprises: 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; receiving second patient data, wherein the second patient data includes a second patient identifier associated with the second patient and a second treatment plan; receiving first measurement data associated with a first performance level of the first treatment plan by the first patient; receiving second measurement data associated with a second performance level of the second treatment plan by the second patient; determining differential data, wherein the determining is based on a contrast of the first or the second measurement data or first or second patient data; and generating, based on the differential data, an instruction to modify an operating state of the treatment apparatus.

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 method for optimizing at least one exercise for a first user. The method includes receiving first user data including attribute data associated with the first user and outcome data associated with the exercise. The method includes generating, based on the first user data, initial target data. The initial target data is associated with at least one of the first user, the exercise apparatus, and the exercise. The method includes receiving measurement data associated with at least one of the first user, the exercise apparatus, and the exercise. The measurement data is associated with one or more sensors. The method includes determining differential data based on one or more differences between the initial target data and the measurement data. The method includes generating, via an artificial intelligence engine and based on the differential data, a machine learning model trained to generate optimized target data.

Abstract: A method that includes receiving treatment data associated with a user capable of using a treatment device to perform a treatment plan. The method also includes receiving user related data (URD) associated with the use and receiving alignment data associated with the user while the user engages in at least one activity. The method also includes identifying, based on at least the treatment data, the URD, and the alignment data, at least one alignment characteristic associated with the user and modifying at least one aspect of the treatment plan in response to receiving, from a healthcare professional, treatment plan input including at least one modification to the at least one aspect of the treatment plan.

Abstract: A system for measuring an angle of a joint of a user includes a center hub, a first arm, a second arm, a magnet, and a sensor. The center hub includes a first hub and a second hub. The first arm is configured for attachment to a first limb portion of the user at a first outer end and to the first hub at a first inner end. The second arm is configured for attachment to a second limb portion of the user at a second outer end and to the second hub at a second inner end, wherein the first hub is pivotally coupled to the second hub. The magnet is coupled to the second hub. The sensor is disposed in the center hub and configured to detect a rotation of the magnet.

Abstract: A system for measuring an angle of a joint of a user includes a center hub, a first arm, a second arm, a magnet, and a sensor. The center hub includes a first hub and a second hub. The first arm is configured for attachment to a first limb portion of the user at a first outer end and to the first hub at a first inner end. The second arm is configured for attachment to a second limb portion of the user at a second outer end and to the second hub at a second inner end, wherein the first hub is pivotally coupled to the second hub. The magnet is coupled to the second hub. The sensor is disposed in the center hub and configured to detect a rotation of the magnet.