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XXVI Brazilian Congress on Biomedical Engineering pp 271–279Cite as

Improving sEMG-Based Hand Gesture Recognition Using Maximal Overlap Discrete Wavelet Transform and an Autoencoder Neural Network

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Part of the book series: IFMBE Proceedings ((IFMBE,volume 70/2))

Abstract

In this work we present a preliminary study regarding the use of MODWT decomposition and time domain parameters for the task of hand gesture classification using sEMG signals. A total of 28 features were extracted for each subject and a first test has shown an improvement of up to 5.8% in relation to previously work. As expected, a simple linear classifier (LDA) obtained the best results. As a second test, we attempted to evaluate the classifiers with respect to the prediction of each hand gesture and mixing the individual data of the subjects. Unlike the first test, with a more complex approach, the Autoencoder technique performed an average accuracy of 77.96% ± 1.24 against only 65.36% ± 1.09 achieved by the LDA classifier. Each classifier fails mainly to separate the gestures belonging to the same grasp group: precision grasp (Tip, Palmar and Lateral) and power grasp (Cylindrical, Hook and Spherical), because of their similarities from a muscular point of view. This result led us to perform a final test considering only precision/power classification, reaching an average accuracy of 95.60% with the Autoencoder Neural Network. In general, the results illustrate that the method presented in this paper can be applied to real applications; but a more refined approach, with more subjects and gestures, should be done.

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Authors and Affiliations

  1. Federal Institute of Espirito Santo, Serra. KM 6.5, Vitória, Espirito Santo, 29173-087, Brazil

    Fernando Henrique Cruz de Andrade, Flavio Garcia Pereira, Cassius Zanetti Resende & Daniel Cruz Cavalieri

Authors
  1. Fernando Henrique Cruz de Andrade
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  2. Flavio Garcia Pereira
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  3. Cassius Zanetti Resende
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  4. Daniel Cruz Cavalieri
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Corresponding author

Correspondence to Daniel Cruz Cavalieri .

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Editors and Affiliations

  1. INMETRO-Instituto Nacional de Metrologia, Qualidade e Tecnologia, Duque de Caxias, Rio de Janeiro, Brazil

    Rodrigo Costa-Felix

  2. Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    João Carlos Machado

  3. INMETRO-Instituto Nacional de Metrologia, Qualidade e Tecnologia, Duque de Caxias, Rio de Janeiro, Brazil

    André Victor Alvarenga

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de Andrade, F.H.C., Pereira, F.G., Resende, C.Z., Cavalieri, D.C. (2019). Improving sEMG-Based Hand Gesture Recognition Using Maximal Overlap Discrete Wavelet Transform and an Autoencoder Neural Network. In: Costa-Felix, R., Machado, J., Alvarenga, A. (eds) XXVI Brazilian Congress on Biomedical Engineering. IFMBE Proceedings, vol 70/2. Springer, Singapore. https://doi.org/10.1007/978-981-13-2517-5_42

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  • DOI: https://doi.org/10.1007/978-981-13-2517-5_42

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2516-8

  • Online ISBN: 978-981-13-2517-5

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