Pattern Recognition of EMG signals in arm movements for Human interface

휴먼 인터페이스를 위한 팔운동 근전신호 패턴인식에 관한 연구

This thesis aims to investigate new approaches to the control strategies of human arm movements and its application for the human interface. By analyzing myoelectric signal(MES) from the arm movements of the normal human subjects, neurological informations obtained patterned could be used to identify different movement patterns of the arm movement. In this paper Artificial neural network for separation of the contraction patterns of four kinds of arm movements, i.e. and flexion and extension of the elbow and adduction and abduction of the forearm were adopted through computer simulation and experiments results were compared with the experimental added-load arm movements.