A prosthesis and control approach using electromyography (EMG) data and motion data. EMG sensors and a motion sensor provide inputs to generate control signals. The EMG sensor detects EMG signals from the user's body. The motion sensor may be one or more inertial measurement sensors (IMS) and/or a magnetic field sensor. The EMG and motion data is analyzed according to various techniques to provide control of one or more actuatable prosthetic joints of an upper limb prosthesis, such as a prosthetic elbow, wrist, hand, and/or digits.

An outer frame for a prosthetic limb is provided. The outer frame is formed from one or more parts and has a plurality of air flow openings.

Metacarpus element for a hand prosthesis,

wherein the metacarpus element comprises at least one connecting element for connecting the metacarpus element to at least one finger frame element, and wherein the metacarpus element comprises a basic metacarpus body and a metacarpus cover, and the basic metacarpus body, together with the metacarpus cover, define a volume.

A rotary drive member drives a driven member. A ring surrounding the driven member has two cam recesses containing lock members between cam surfaces of cam recesses and the ring. Each recess accommodates an associated lock member at different locations where the recess is shallower. Driving the driven member is permitted in a given rotation relative to the ring, but each lock member inhibits rotation of the driven member in the opposite sense. The recesses extend in opposite directions. Coupling between the driven members is free-play whereby reversal in the rotation disengages members and reengages. Protuberances extending into cam recesses retain a lock member associated with one recess at the deeper location permitting movement of the other lock member towards the shallower location. Upon reversal of the rotation, the protuberances retain another lock member at the deeper location permitting movement of one lock member.

Prosthetic devices, such as prosthetic hands (including, e.g., bionic prosthetic hands controlled by myoelectric signals and/or equipped with force sensors for feedback) can utilize a modular design to simplify assembly and repair. In some embodiments, low-cost additive manufacturing techniques are employed, e.g., to create prosthetic device parts with complex interior geometries and/or functionally integrated components.

A neurostimulator incorporating a novel chip design that uses the principle of redundant signal crossfiring to overcome electronic component mismatch error in general and transistor mismatch error in particular, to yield superior quality neurostimulation signal generation, useful in enhancing the bidirectional human-machine interface in prosthesis operation for the restoration of somatosensation for an amputee.

A covering shell for a prosthesis of a given limb, the shell having at least two zones of different flexibility. The arrangement of the zones of the covering shell in relation to one another corresponding to the arrangement of the parts of the given limb having different hardnesses. An exoskeletal structure, having preferably a tubular shape, of a prosthesis of a given limb. The exoskeletal structure being designed to provide a connection between a socket and a hand prosthesis or between a socket and a foot prosthesis, in order to form the prosthesis of the limb. The exoskeletal structure having at least two zones of different flexibility. The arrangement of the zones of the exoskeletal structure in relation to one another corresponding to the arrangement of the parts of the given limb having different hardnesses.

A hand prosthesis base body having an outer side, a motor, and a first rotatable shaft which is connected with an output shaft of the motor. The first rotatable shaft has a coupling element for a detachably coupling at least one finger element to the rotatable shaft.

Provided are a multi-degree-of-freedom myoelectric artificial hand control system and a method for using same. The system comprises a robotic hand, a robotic wrist (2), a stump receiving cavity (1) and a data processor (3), wherein the robotic hand and the stump receiving cavity (1) are respectively mounted on two ends of the robotic wrist (2); a multi-channel myoelectric array electrode oversleeve, a control unit circuit board, and a battery are connected in the stump receiving cavity (1); and the other end of the control unit circuit board is connected to the robotic hand and the robotic wrist (2). The method for using the system comprises the following steps: (S1) a user wearing a multi-channel myoelectric array electrode oversleeve, and connecting a battery and a control unit circuit board; (S2) the user completing a gesture, collecting a surface electromyography signal and then uploading same to a data processor (3); (S3) the data processor (3) receiving the surface electromyography signal and inputting same into a neural network algorithm to generate a gesture prediction model; and (S4) the user controlling the multi-degree-of-freedom movement of the robotic wrist (2) and the robotic hand. By means of the system, continuous gestures and the gesture strength thereof can be identified, and multi-degree-of-freedom gestures can be made.

A mechanical finger has a base. A proximal phalanx and a distal phalanx is provided with a first epicyclic joint between the base and proximal phalanx, and a second epicyclic joint between the proximal phalanx and the distal phalanx. A distal four-bar mechanism includes the proximal phalanx, the distal four-bar mechanism coupled to the distal phalanx. A nail is optionally provided and has a joint mechanism movably connecting the nail to the distal most one of the phalanges between a stowed position in which a grasping tip of the nail is concealed in the distal most one of the phalanges, and a deployed configuration in which the grasping tip projects out of the distal most one of the phalanges.