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.

Artificial limbs and joints that behave like biological limbs and joints employ a synthetic actuator which consumes negligible power when exerting zero force, consumes negligible power when outputting force at constant length (isometric) and while performing dissipative, nonconservative work, is capable of independently engaging flexion and extension tendon-like, series springs, is capable of independently varying joint position and stiffness, and exploits series elasticity for mechanical power amplification.

Artificial limbs and joints that behave like biological limbs and joints employ a synthetic actuator which consumes negligible power when exerting zero force, consumes negligible power when outputting force at constant length (isometric) and while performing dissipative, nonconservative work, is capable of independently engaging flexion and extension tendon-like, series springs, is capable of independently varying joint position and stiffness, and exploits series elasticity for mechanical power amplification.

Routines and methods disclosed herein can increase a power efficiency of a prosthetic hand without drastically reducing the speed at which it operates. A prosthesis can implement an acceleration profile, which can reduce an energy consumption of a motor, or an amount of electrical and/or mechanical noise produced by a motor, as the motor as the motor transitions from an idle state to a non-idle state. A prosthesis can implement a deceleration profile, which can reduce the energy consumption of the motor, or an amount of electrical and/or mechanical noise produced by a motor, as the motor transitions from a non-idle state to an idle state.

Routines and methods disclosed herein can increase a power efficiency of a prosthetic hand without drastically reducing the speed at which it operates. A prosthesis can implement an acceleration profile, which can reduce an energy consumption of a motor, or an amount of electrical and/or mechanical noise produced by a motor, as the motor as the motor transitions from an idle state to a non-idle state. A prosthesis can implement a deceleration profile, which can reduce the energy consumption of the motor, or an amount of electrical and/or mechanical noise produced by a motor, as the motor transitions from a non-idle state to an idle state.

Examples of a device for guiding and detecting a motion of a target joints and a motion assistance system such motion guiding devices are described. The motion guiding and detecting device comprises a motion generator and a motion transfer and target interfacing unit to transfer the motion generated by the motion generator to the target joint. The system further includes a motion detection and feedback unit that interfaces with the target, and a controller that interfaces with both the feedback unit and the motion generator to control and coordinate the motion of the motion generator and the target joint.

Examples of a device for guiding and detecting a motion of a target joints and a motion assistance system such motion guiding devices are described. The motion guiding and detecting device comprises a motion generator and a motion transfer and target interfacing unit to transfer the motion generated by the motion generator to the target joint. The system further includes a motion detection and feedback unit that interfaces with the target, and a controller that interfaces with both the feedback unit and the motion generator to control and coordinate the motion of the motion generator and the target joint.

A prosthetic limb in amputation rehabilitation, having a forearm and a hand with four fingers and a thumb, with the wrist and the fingers & thumb thereof being fully independently controlled by nerve signals originating in the amputee's brain and not being controlled by the actions of nearby muscles in the amputee's upper arm or shoulder. Control of the prosthesis is achieved by a fully contained electronic unit in the forearm of the prosthesis that receives neural signals from the brain, converts the analog neural signals to digital signals that are fed into an artificial intelligence engine circuit that utilizes a library of algorithms to learn from the brain what the signals are that will produce a desired hand and finger movement, then convert its computed digital output to analog electrical signals that are fed to the prosthetic hand and finger to produce actual motion as instructed by the brain.

A prosthetic limb in amputation rehabilitation, having a forearm and a hand with four fingers and a thumb, with the wrist and the fingers & thumb thereof being fully independently controlled by nerve signals originating in the amputee's brain and not being controlled by the actions of nearby muscles in the amputee's upper arm or shoulder. Control of the prosthesis is achieved by a fully contained electronic unit in the forearm of the prosthesis that receives neural signals from the brain, converts the analog neural signals to digital signals that are fed into an artificial intelligence engine circuit that utilizes a library of algorithms to learn from the brain what the signals are that will produce a desired hand and finger movement, then convert its computed digital output to analog electrical signals that are fed to the prosthetic hand and finger to produce actual motion as instructed by the brain.

A conversion device includes: a unit attribute update unit that specifies a unit associated with a representative value approximating a feature vector for a biological signal, and updates a representative value and energy of the specified unit; a unit update unit that, when a plurality of feature vectors outside a predetermined range of a representative value of a unit of the unit data are acquired within a predetermined time, adds data for a new unit identifier to unit data; a class update unit that, when there is no class including a unit having energy within a predetermined range of energy of the new unit, adds data for a new class identifier to class data; and a motion update unit that updates motion data by associating, with an identifier of a class acquired after updating the class data, an identifier of a motion corresponding to the class.