Adaptive Switching in Practice: Improving Myoelectric Prosthesis Performance through Reinforcement Learning

Myoelectrically controlled prostheses are a class of assistive device that use electrical signals generated by muscle activation. These electromyographic (EMG) signals are used to control one or more electromechanical actuators that move prosthetic joints. Myoelectric control signals are typically measured with electrodes on the surface of the skin, with one pair of electrodes over each muscle site. In this manner, each muscle site directly controls one motion of the prosthesis, and various methods of switching can be used as needed to control additional motions of the prosthesis [1] [2] [3].