SEMG Signals BP Neural Network Classification Based on Wavelet Packet Energy

DOI

10.2991/bbe-16.2016.11How to use a DOI?

Keywords

Surface EMG, Wavelet transform, Wavelet packet, BP neural network.

Abstract

The purpose of this paper is to study the different features of surface electromyography signals while subjects take different actions with their forearms. In this paper, the surface electromyography signals, recorded from some healthy volunteers under different actions (fist, fist exhibition, inside wrist pronation and wrist supination), were first denoised by wavelet packet transform. And then we extracted the features of the surface electromyography signals. Finally, the BP neural network was utilized to classify the features so as to distinguish different forearm actions. These results can be used as reference data in muscular diseases and disability in patients treatment or diagnosis .In this article, it was the first time to study surface electromyography signals, and this signal was generate by healthy volunteer under different action of fist ,fist exhibition inside wrist pronation and wrist supination .The method of this study could be used in produced intelligent prostheses.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Liuyang Xu
AU  - Lijuan Shi
AU  - Yi Yu
AU  - Xinqi He
AU  - Yun Zhao
AU  - Juntang Lin
PY  - 2016/07
DA  - 2016/07
TI  - SEMG Signals BP Neural Network Classification Based on Wavelet Packet Energy
BT  - Proceedings of the 2016 International Conference on Biomedical and Biological Engineering
PB  - Atlantis Press
SP  - 56
EP  - 61
SN  - 2468-5747
UR  - https://doi.org/10.2991/bbe-16.2016.11
DO  - 10.2991/bbe-16.2016.11
ID  - Xu2016/07
ER  -