The present invention relates to an assisted rehabilitation system comprising a device with static-bicycle movement, and devices that automatically sense and manipulate body performance parameters, allowing the rehabilitation response process of a user to be optimised. In particular, the sensing devices measure the pedalling rate, the pedalling resistance and the user's pulse rate, allowing the optimum exercising range to be configured, depending on the data acquired from the patient and on the level of effort required from him or her. The system also comprises two motors. One motor drives a flywheel that allows the user to experience a variation in the pedalling rate while using the device. A second motor provides different levels of pedalling resistance, allowing muscular strength to be developed. When starting the activity, the user enters, on a touchscreen, the preliminary parameters for the desired pedalling rate and resistance level, which are analysed by a processing unit that calculates a suitable rehabilitation plan.

A computer-implemented system includes one or more processing devices configured to receive user information associated with a user, generate a selected set of the user information, determine, based on the selected set of the user information, at least one of a first probability of surviving one or more procedures and a second probability indicating an improvement, resulting from the one or more procedures, in quality-of-life metrics for the user, generate, based on the at least one of the first probability and the second probability and on the selected set of the user information, one or more recommendations of whether the user should undergo the one or more procedures, and generate, based on the one or more recommendations, a treatment plan that includes one or more exercises directed to modifying the at least one of the first probability and the second probability.

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 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.

An electromechanical device for rehabilitation includes a motor and a control system including one or more processing devices operatively coupled to the motor. The one or more processing devices are configured to, responsive to a first trigger condition occurring, control the motor to operate in a passive mode, responsive to a second trigger condition occurring, control the motor to operate in an active-assisted mode, and responsive to a third trigger condition occurring, control the motor, by providing resistance, to operate in a resistive mode.

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.

A translational-rotary machine is provided as a physical exercise machine particularly for the rehabilitation or training of upper and lower limbs for both passive and active therapies. The machine can perform movements that: allow a lower shaft to rotate while the carriage is stationary; allow the lower shaft to not rotate while the carriage translates in a direction of applied force; or allow a translational-rotary movement.

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.