A pedal slider assembly that allows for a normal pedal affixed to a normal bicycle to slide laterally outwardly away from the bicycle frame and back inwardly towards the bicycle frame allowing the rider to find their bodies natural pedal placement as it travels through the rotational pedaling path based on their specific skeletal structure restrictions, (those being the width of an individual's hips, knees, and ankles) as those positions dictate the center line of the user and as those positions are directly affected by the pedal relationship to the center line of the bicycle frame, additionally there is a multitude of lateral movement length options for the rider to utilize to garner specific exercise benefits.

A drag gain structure for the gravity wheel of fitness equipment includes an eccentric driving member disposed on the first side of the gravity wheel, which includes inner and outer side plate sections, and an interconnecting piece. The interconnecting piece is rotationally coupled to at least one of the plate sections. One end of outer side plate section is fitted over and fixed to the shaft, and one end of inner side plate section is connected to an eccentric position of gravity wheel through the first bias joint pin. A bearing pedestal is disposed on the second side of the gravity wheel, including a bearing screwed on the shaft, a pedestal shell fitted over the bearing and a radial protruding plate on the periphery of pedestal shell. The protruding end of the radial protruding plate is connected to an eccentric position of gravity wheel through the second bias joint pin.

Provided are apparatuses for augmenting human speed, human-driven compliant mechanisms, artificial limbs to augment human movement, and the like. The apparatuses can include variable stiffness mechanisms including springs that can be configured to selectively store and subsequently release energy generated by human movement. The apparatuses can selectively release the captured energy supplied by the human to provide force and power output beyond the physical capability of the human.

A stationary indoor “smart” training bicycle includes a unique combination of adjustable components to provide configurable dimensions to adjust the frame size of the indoor bicycle to properly fit a rider. A system is also provided to process a digital image of an outdoor bicycle and determine and translate dimensions and adjustments to the indoor bicycle to match one or more dimensions (lengths, angles, separations, etc.) of the outdoor bicycle.

Systems, methods, and computer-readable mediums for operating an electromechanical device are disclosed. The system includes, in one example, the electromechanical device, a patient portal, and a computing device. The computing device is configured to receive user data relating to a user, and receive treatment data relating to treatment plans and outcomes. The computing device is also configured to generate a prehabilitation plan by using a machine learning model to process the user data and the treatment data. The computing device is further configured to select, for the electromechanical device, an electromechanical device configuration that enables exercises of the prehabilitation plan to be performed by the user such that performance improves an area of the user's body. The computing device is also configured to enable the electromechanical device to implement the electromechanical device configuration.

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.

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.