Real-time Classification of Finger Movements using Two-channel Surface Electromyography

Khairul Anam, Adel Al-Jumaily

Abstract

The use of a small number of Electromyography (EMG) channels for classifying the finger movement is a challenging task. This paper proposes the recognition system for decoding the individual and combined finger movements using two channels surface EMG. The proposed system utilizes Spectral Regression Discriminant Analysis (SRDA) for dimensionality reduction, Extreme Learning Machine (ELM) for classification and the majority vote for the classification smoothness. The experimental results show that the proposed system was able to classify ten classes of individual and combined finger movements, offline and online with accuracy 97.96 % and 97.07% respectively.

References

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Paper Citation


in Harvard Style

Anam K. and Al-Jumaily A. (2013). Real-time Classification of Finger Movements using Two-channel Surface Electromyography . In Proceedings of the International Congress on Neurotechnology, Electronics and Informatics - Volume 1: RoboAssist, (NEUROTECHNIX 2013) ISBN 978-989-8565-80-8, pages 218-223. DOI: 10.5220/0004663002180223


in Bibtex Style

@conference{roboassist13,
author={Khairul Anam and Adel Al-Jumaily},
title={Real-time Classification of Finger Movements using Two-channel Surface Electromyography},
booktitle={Proceedings of the International Congress on Neurotechnology, Electronics and Informatics - Volume 1: RoboAssist, (NEUROTECHNIX 2013)},
year={2013},
pages={218-223},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004663002180223},
isbn={978-989-8565-80-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Congress on Neurotechnology, Electronics and Informatics - Volume 1: RoboAssist, (NEUROTECHNIX 2013)
TI - Real-time Classification of Finger Movements using Two-channel Surface Electromyography
SN - 978-989-8565-80-8
AU - Anam K.
AU - Al-Jumaily A.
PY - 2013
SP - 218
EP - 223
DO - 10.5220/0004663002180223