Clench force estimation by surface electromyography for neural prosthesis hand

Author

Mostafa, Sheikh Shanawaz ; Ahmad, Mohiuddin ; Awal, Md Abdul

Author_Institution

Dept. of Biomed. Eng., Khulna Univ. of Eng. & Technol., Khulna, Bangladesh

fYear

2012

fDate

18-19 May 2012

Firstpage

505

Lastpage

510

Abstract

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.

Keywords

backpropagation; electromyography; estimation theory; medical signal processing; neural nets; neurophysiology; prosthetics; rectification; ANN; Levenberg-Marquardt back propagation algorithm; SEMG recording; clench force estimation; cross-correlation coefficient; integrated SEMG; neural prosthesis hand; rectified SEMG; surface electromyography; two-layered artificial neural network; Force; Force measurement; Propagation losses; Silicon; Time measurement; ANN; L-M back propagration; SEMG; clench force; neural prosthesis hand;

fLanguage

English

Publisher

ieee

Conference_Titel

Informatics, Electronics & Vision (ICIEV), 2012 International Conference on

Conference_Location

Dhaka

Print_ISBN

978-1-4673-1153-3

Type

conf

DOI

10.1109/ICIEV.2012.6317489

Filename

6317489