Grasp Recognition From Myoelectric Signals

Current myoelectric prosthetic hands enable the user to enact a simple grasp with a strength proportional to the contraction of certain muscle groups. This paper discusses the development of a system that will allow complex grasp shapes to be identified based on natural muscle movement. The application of this system can be extended to a general device controller where input is obtained from forearm muscles, measured using unobtrusive surface electrodes. This system provides the advantage of being less fatiguing than traditional input devices. The instrumentation hardware, computer software and algorithms developed to achieve this task are described in this paper.