Characterization of Myoelectric Signals Using System Identification Techniques

Human muscle motion is initiated in the central nervous system where a nervous signal travels through the body and the motor neurons excite the muscles to move. These signals, termed myoelectric signals, can be measured on the surface of the skin as an electrical potential. By analyzing these signals it is possible to determine the muscle actions the signals elicit, and thus can be used in manipulating smart prostheses and teleoperation of machinery. Due to the randomness of myoelectric signals, identification of the signals is not complete, therefore the goal of this project is to complete a study of the characterization of one set of hand motions using current system identification methods. The gripping motion of the hand and the corresponding myoelectric signals are measured and the data captured with a personal computer. Using computer software the captured data are processed and finally subjected to several system identification routines. Using this technique it is possible to construct a mathematical model that correlates the myoelectric signals with the matching hand motion.