Gestures Recognition Method Based on Electromyographic Signal

DOI

10.2991/asei-15.2015.359How to use a DOI?

Keywords

EMGs; Power frequency interference; Wavelet transform; Neural networks

Abstract

Different gestures were identified through analyzing and processing the electromyographic signal(EMGs) collected from the forearm. That in turn was used to control the upper limb rehabilitation equipment. The wavelet denoising was used after filtering the power frequency interference and the normalized processing. The high and low frequency coefficients were decomposed from signal through wavelet transform. The variance calculated from the frequency coefficients was used as a characteristic value. Through the neural networks classification, the recognition rates of seven kinds of gestures are over 99%. The seven kinds of gestures were wrist inward, wrist outward, fist stretch, fist clench, wrist up, wrist down and palm downward spiral.

Copyright

© 2015, 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/).

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TY  - CONF
AU  - Yucheng Tian
AU  - Mo Wang
AU  - Xing Zhang
AU  - Xin’an Wang
PY  - 2015/05
DA  - 2015/05
TI  - Gestures Recognition Method Based on Electromyographic Signal
BT  - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation
PB  - Atlantis Press
SP  - 1804
EP  - 1809
SN  - 2352-5401
UR  - https://doi.org/10.2991/asei-15.2015.359
DO  - 10.2991/asei-15.2015.359
ID  - Tian2015/05
ER  -