A walking assist device includes: a pair of right and left handles that are movable back and forth with respect to a frame in accordance with arm swing performed during walk of a user; rails on which the handles are provided and which limit movement of the handles in a movable range in accordance with arm swing performed during the walk of the user; a handle information acquisition unit that acquires information related to movement of the handles; and a control unit that controls a drive unit in accordance with the information from the handle information acquisition unit.

A mechanical foot includes a mechanical ankle joint including a leg portion configured to be fixed to a patient's leg, a foot portion configured to be fixed to the patient's foot, and a revolute. The leg portion and the foot portion are connected through the revolute in a manner of being rotatable relative to each other. The revolute includes a foot connecting portion connected to the foot portion and a leg connecting portion connected to the leg portion, and the foot connecting portion and the leg connecting portion are connected in a manner of being rotatable relative to each other. The revolute further includes a first elastic element having one end connected to the foot connecting portion, and the other end connected to the leg connecting portion, so that a force maintaining the patient's foot substantially horizontal is provided through the revolute at a position of the mechanical ankle joint.

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.

A method for optimizing at least one exercise. An exercise apparatus is configured to enable a user to perform the at least one exercise. The method includes receiving user data. The method includes generating, based on the user data, initial target data. The method includes receiving measurement data associated with one or more sensors. The method includes determining, differential data. The determining is based on one or more differences between the initial target data and the measurement data. The method includes receiving cohort data. The method includes generating, via an artificial intelligence engine and based on the differential data, a machine learning model trained to generate message data based on a difference between the differential data and the cohort data. The method includes transmitting, to an interface associated with a user, a message to the user based on the message data.

A method includes receiving treatment data associated with a user capable of using a treatment device to perform a treatment plan. The method also includes receiving alignment data associated with the user while the user engages in at least one activity and receiving at least one alignment characteristic associated with the user and determining, using at least the at least one alignment characteristic, whether the at least one alignment characteristic correlates with at least one secondary condition of the user. The method also includes generating secondary condition information indicating at least the secondary condition and modifying at least one aspect of the treatment plan in response to receiving, from a healthcare professional, treatment plan input. The treatment plan input includes at least one modification to the at least one aspect of the treatment plan and wherein, further, the treatment plan input is generated based on the secondary condition information.

A device for training and rehabilitation of a limb is provided. The device provides a board with a plurality of movement tracks to allow for controlled movement of the limb in various directions. Blockers and other controlling structures may be arranged on the device to limit range of motion of the movement of the limb.

Described herein are examples of multi-use mounting brackets having an extension with an internal support rod configured to support gym accessories. A single multi-use mounting bracket fits gym rigging or gym racks of different sizes and hole configurations. This accomplished by including multiple pairs of holes that are sized and positioned to be compatible with different gym rigging configurations. The multi-use mounting brackets described herein are advantageously designed to resist both downward and upward forces.

A walking training device according to the present embodiment includes: a robot leg attached to one leg of a trainee; a treadmill; a load distribution sensor that detects a distribution of a load received from a sole of the trainee riding on the belt of the treadmill; and a walking state determination unit that determines whether the one leg has switched from a standing state to a swinging state based on a state of increase in a load detected by the load distribution sensor and received from another leg of the trainee performing walking training; and a control unit that starts bending control for the swinging state of the robot leg when the walking state determination unit determines that the one leg has switched from the standing state to the swinging state.

A pegboard of the present disclosure includes: a main device including multiple unit modules; a board plate including multiple receiving portions; and multiple pegs to be inserted into the multiple receiving portions, and the multiple unit modules include multiple sensor modules configured to sense whether the multiple pegs are inserted into the multiple receiving portions and multiple light source modules configured to output light to the multiple receiving portions.

A force assistance system for an exercise machine assists a user during a first portion of a lift. The force assistance system provides an assistance force at the beginning of a lift to help the user perform the lift. The assistance force reduces the amount of force the user must provide to start the lift. The assistance force helps the user exercise using higher weights for better muscle development. The force assistance system for the exercise machine generally includes an upper member, a lower member and at least one resilient member positioned between the upper member and the lower member. The upper member, the lower member and the at least one resilient member are adapted to be positioned between a frame and a movable arm of the exercise machine. The at least one resilient member applies the assistance force in an upward direction to the movable arm to help the user move the movable arm during a first portion of a lift.