Clench force estimation by surface electromyography for neural prosthesis hand

Clench force estimator is highly desirable in the field of prosthesis hand. It is one of the most used postures among five types of postures. In this paper, we propose to estimate the clench force using two types of Surface Electromyography (SEMG). The SMEG consists of rectified SEMG and integrated SEMG. A two layered artificial neural network (ANN) is used as an estimator to map the SEMG for estimating force. For weight adjustment of the estimator Levenberg-Marquardt (L-M) back propagation algorithm is used. The proposed network is trained and tested using SEMG recorded from five subjects. The estimation result clearly show that integrated SEMG performed 3.53 times better than rectified SEMG in the case of cross correlation coefficient and hence integrated SEMG is recommended for clench force estimation.