A neural network-based surface electromyography motion pattern classifier for the control of prostheses

Author

Wang, Rencheng ; Huang, Changhua ; Li, Bo

Author_Institution

Dept. of Precision Instrum., Tsinghua Univ., Beijing, China

Volume

3

fYear

1997

fDate

30 Oct-2 Nov 1997

Firstpage

1275

Abstract

This paper presents a surface electromyography (EMG) motion pattern classifier which combines an artificial neural network (ANN) with a parametric model such as an autoregressive (AR) model. This motion pattern classifier can successfully identify four types of movement of human hand, wrist flexion, wrist extension, forearm pronation and forearm supination, by using the surface EMG detected from the flexor carpi radialis and the extensor carpi ulnaris. This desirable result shows that it have a great potential application to our Tsinghua multi-degree artificial hand

Keywords

artificial limbs; autoregressive processes; backpropagation; biocontrol; electromyography; gradient methods; medical signal processing; motion control; neural nets; neuromuscular stimulation; pattern classification; Tsinghua multi-degree artificial hand; artificial neural network; autoregressive model; backpropagation; extensor carpi ulnaris; flexor carpi radialis; forearm pronation; forearm supination; gradient optimum method; human hand movement identification; parametric model; prostheses control; surface EMG motion pattern classifier; wrist extension; wrist flexion; Artificial neural networks; Bioelectric phenomena; Electrodes; Electromyography; Motion control; Motion detection; Neural networks; Neural prosthesis; Prosthetics; Wrist;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE

Conference_Location

Chicago, IL

ISSN

1094-687X

Print_ISBN

0-7803-4262-3

Type

conf

DOI

10.1109/IEMBS.1997.756607

Filename

756607