Neural Networks with AR Model Coefficients Applied to the EMG Signal Classification

Marek Kurzynski, Andrzej Wolczowski

2010

Abstract

The paper presents a concept of hand movements recognition on the basis of EMG signal analysis. Signal features are represented by coefficient of autoregressive (AR) model, and as classifier the MLP and Adaline networks are applied. The performance of the proposed method was experimentally compared against four different classifiers using real datasets. The systems developed achieved the highest overall classification accuracies demonstrating the potential of neural network classifiers based on AR coefficients for recognition of EMG signals.

References

  1. Boostani R., Moradi M., Evaluation of the forearm EMG signal features for the control of a prosthetic hand, Physiological Measurement 24 (2003), 309-319
  2. De Luca C., J., Adam R., et al., Decomposition of surface EMG signals, Journal of Neuropsychology 96, (2006) 1646-1657
  3. Dietterich T.G., Approximate statistical tests for comparing supervised classification learning algorithms, Neural Computing 10 (1998) 1895-1923
  4. Duda R., Hart P., Stork D., Pattern Classification, John Wiley (2000)
  5. Ferguson S., Dunlop G., Grasp recognition from myoelectric signals, Proc. 2002 Australasian Conference on Robotics and Automation (2002) 83-87
  6. Kuribayachi K., Okimura K. and Taniquichi T., A discrimination system using neural network for EMG-controlled prostheses, Proc. Int. Conf. IEEE Robot and Human Communication (1992) 63-68
  7. Khoshaba T., Badie K., and Hashemi R., EMG pattern classification based on back propagation neural network for prosthesis control, Proc. Ann. Int. Conf. IEEE Eng. Med. Biol. Soc. (1990) 1474-1475
  8. Reaz M., Hussain M., Techniques of EMG signal analysis: detection, processing, classification and applications, Biological Procedures Online 8 (2006) 11-35
  9. Schloegl A., A comparison of multivariate autoregressive estimators, Signal Processing 9 (2006) 2426-2429
  10. Wang, G., Zhiguo, Y., Xiao, H., et al., Classification of surface EMG signals using harmonic wavelet packet transform, Physiol. Measurement 27 (2006) 1255-1267
  11. Wojtczak P., Amaral T., Dias O., Hand movement recognition based on biosignal analysis, Engineering Applications of Artificial Intelligence 22 (2009) 608-615
  12. Wolczowski A., Krysztoforski K., Control-measurement circuit of myoelectric prosthesis hand, Acta Bioengineering and Biomechanics 4 (Suppl.) (2002) 576-578
  13. Wolczowski A., Kurzynski M., Human-machine interface in bioprosthesis control using EMG signal classification, Expert Systems 27, (2010) 53-70
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Paper Citation


in Harvard Style

Kurzynski M. and Wolczowski A. (2010). Neural Networks with AR Model Coefficients Applied to the EMG Signal Classification . In Proceedings of the 6th International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: Workshop ANNIIP, (ICINCO 2010) ISBN 978-989-8425-03-4, pages 81-86. DOI: 10.5220/0003022900810086


in Bibtex Style

@conference{workshop anniip10,
author={Marek Kurzynski and Andrzej Wolczowski},
title={Neural Networks with AR Model Coefficients Applied to the EMG Signal Classification},
booktitle={Proceedings of the 6th International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: Workshop ANNIIP, (ICINCO 2010)},
year={2010},
pages={81-86},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003022900810086},
isbn={978-989-8425-03-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: Workshop ANNIIP, (ICINCO 2010)
TI - Neural Networks with AR Model Coefficients Applied to the EMG Signal Classification
SN - 978-989-8425-03-4
AU - Kurzynski M.
AU - Wolczowski A.
PY - 2010
SP - 81
EP - 86
DO - 10.5220/0003022900810086