New EMG Pattern Recognition based on Soft Computing Techniques and Its Application to Control of a Rehabilitation Robotic Arm

A new EMG pattern classification method based on soft computing techniques is proposed to help the disabled and the elderly handle rehabilitation robotic arm systems. First, it is shown that EMG is more useful than existing input devices, such as voice, a laser pointer, and a keypad in view of naturality, extensibility, and applicability. Next, through soft computing techniques, such as the fuzzy logic and rough set theory, a new procedure is proposed to select an essential feature set of EMG signals that is independent of users. In order to classify pre-defined motions, a fuzzy pattern classification and fuzzy min-max neural networks (FMMNN) are adopted to handle the selected minimal feature set in systematical ways. As results, motions are recognized with success rates of 83 percent and 90 percent for fuzzy pattern classification and FMMNN, respectively.