There is provided an IoT therapy armchair, in unitary form, with a chassis having a back section, a pair of side sections connected to the back section with an armrest on a top surface of each side section with the back section and pair of sides defining a seating area. A front leg support section extends downward from and below the seating area and connects each of the side sections. A plurality of pressure hangers fastened to the chassis, each of which are capable of receiving an exercise accessory, each connected to a respective pressure transducer, that measures an amount of pressure applied to each of the plurality of pressure hangers. The amount of pressure applied and health sensors are communicated as an output signal to a computing device that create statistics, reports of health conditions, early warnings, alarms, and communicates online and provides interactive therapy guidance to users.

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.

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.

A computer-implemented system includes a treatment device configured to be manipulated by a user while the user performs a treatment plan, a patient interface, and a computing device configured to: receive treatment; write to an associated memory, configured to be accessed by an artificial intelligence engine, treatment data, the artificial intelligence engine being configured to use at least one machine learning model to, using the treatment data, generate at least one of a treatment scheduling output prediction and an appointment output; receive, from the artificial intelligence engine, the at least one of the treatment scheduling output prediction and the appointment output; and selectively modify, using the at least one of the treatment scheduling output prediction and the appointment output, the at least one aspect of the treatment plan.

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 comprising an output device configured to present telemedicine information associated with a telemedicine session, 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 at least one measurement pertaining to the user associated with a first treatment device mode of the treatment device; determine whether the at least one aspect of the measurement correlates with a secondary condition of the user; and, in response to a determination that the at least one aspect of the at least one measurement is correlated with the at least one secondary condition of the user, generate secondary condition information indicating at least the secondary condition.

The therapeutic isometric testing and isotonic training exercise device disclosed herein may allow for isometric strength testing and isometric and isotonic strength training of a user to prevent or treat soft and connective tissue deficiencies. The device may comprise, generally, a leg press, chest press, and shrug press exercise device combination having a plurality of electronic components. The device may comprise a seat assembly and weight stack assembly connected to an articulating frame, and may further comprise a weight locking pin and a plurality of load sensors for user work measurements. A proprietary software may be integrated into the device, and each user may have a personal user account and customized training regimen designed for them based on system inputs and readings.

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.

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.

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.

A computer-implemented system includes a treatment device configured to be manipulated by an individual while the individual performs a treatment plan, a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session, and a computing device configured to: identify two or more types of healthcare providers to participate during the telemedicine session in the treatment plan associated with the individual; identify at least one healthcare provider associated with each of the two or more types of the healthcare providers; identify, based on schedules of the identified at least one healthcare provider, one or more available time slots for each respective healthcare provider; identify a first time slot of the available time slots; generate, based on the first time slot, a schedule event; and communicate the schedule event to each of the identified healthcare providers.