A motion assistance apparatus includes a proximal frame configured to support a proximal part of a user, a connecting frame rotatably connected to the proximal frame, a distal frame rotatably connected to the connecting frame and configured to support a distal part of the user, and a power transmitter including a slider configured to slide along the proximal frame, a pusher rotatably connected to the distal frame and the slider, and a coupler configured to connect the slider and the pusher and positioned closer to the proximal part of the user than a rotation axis of the connecting frame in the whole range of motion.

A walking assist device applies resistance force to a movement of a knee joint of a user according to a walking state of the user to assist a walking motion of the user. The walking assist device includes an auxiliary control unit that decreases the resistance force applied to the movement of the knee joint of the user when the walking state of the user transitions from a standing phase to a swinging phase, and a knee collapse sensing unit that senses a knee collapse by detecting that the knee joint of the user is located forward of a waist portion of the user during the standing phase. When the knee collapse sensing unit senses the knee collapse, the auxiliary force control unit reduces a degree of a decrease in the resistance force or stops the decrease in the resistance force.

A pivoting lower limb therapy device formed from a lever frame having a handle portion integral with its proximal end and a first orthotic portion adjacent to its distal end, a base frame having a second orthotic portion integral with its proximal frame end and a surface support portion integral with its distal frame end, with the lever frame rotatably attached to the base frame so as to permit the first orthotic portion to move across two dimensions relative to the second orthotic portion in response to an exertion of mechanical force on the handle portion. The pivoting lower limb therapy device may also include a strap assembly connected to and extending between the proximal end of the lever frame and the proximal frame end of the base frame so as to exert mechanical force on the two frame members.

An exoskeleton joint self-locking mechanism, a knee joint and a bionic rehabilitation robot are provided. The self-locking mechanism comprises a first base, a rotating outward expanding locking member, a second base and a locking driving member; the rotating outward expanding locking member comprises a first rotating frame and a second rotating frame, and outer sides of the first rotating frame and the second rotating frame have a first friction surface; one end of the first rotating frame is pivoted with one end of the second rotating frame; the second base is rotationally mounted on the first base, and an inner wall of the second base defines a second friction surface enclosing the first friction surface; the locking driving member applies/removes a force pushing away from free ends of the first rotating frame and the second rotating frame, to make the first friction surface lock/unlock the second friction surface.

An exoskeleton device that includes an artificial joint and a frame member extending from the artificial joint. The frame member is configured for extension over a limb of a user. The exoskeleton device also includes a self-aligning mechanism connected to the frame member. The self-aligning mechanism includes three passive degrees of freedom (pDOF) provided in a prismatic-revolute-revolute (PRR) configuration. The self-aligning mechanism also includes a limb attachment member configured for mechanically coupling to a portion of the limb of the user.

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.

The invention relates to a wearable device for determining a nature of motion and/or a physiological state of a wearer. The wearable device comprises a body portion configured to be worn by the wearer and at least one sensor mounted to the body portion configured to, while the wearable device is being worn, detect a motion parameter of the wearer and/or a physiological parameter of the wearer and generate input data indicative of the motion parameter and/or the physiological parameter. The wearable device further comprises a processor configured to receive the input data from the at least one sensor and process the input data by executing an algorithm to determine the nature of motion and/or the physiological state of the wearer while the wearable device is being worn. The invention further relates to a system for determining a nature of motion and/or a physiological state of a wearer.

A knee movement support device according to an embodiment of the present disclosure is a knee movement support device attached to a leg of a user, and includes: an upper leg link fastened to an upper leg of the user; a lower leg link fastened to a lower leg of the user; and an interval adjusting portion disposed between the upper leg of the user and the upper leg link or between the lower leg of the user and the lower leg link.

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.