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Lower Limb Movement Recognition Using EMG Signals

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Intelligent Systems Design and Applications (ISDA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 418))

Abstract

This paper presents an enhanced extraction feature for lower limb movement recognition application using surface EMG signals. Public SEMG database is used for system evaluation, where subjects, depending on knee normality, are divided into normal and abnormal groups. The spectogram of input EMG signals are calculated in time-frequency domain, and then processed with standard deviation texture. Experimental results show that EMG data of Semitendinosus (ST) muscle with Convolutional Neural Network (CNN) classifier provide the highest accuracy of 92% for classifying up to three movements (gait, leg extension, leg flexion) in normal group, and 95% for classifying two movements (gait, leg flexion) in abnormal group.

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Author information

Authors and Affiliations

  1. Physics, Mechanical and Electrical Engineering, Hubei University of Education, Wuhan, China

    Sali Issa

  2. Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Abdel Rohman Khaled

Authors
  1. Sali Issa
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  2. Abdel Rohman Khaled
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Corresponding author

Correspondence to Sali Issa .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  3. Institut für Wirtschaftsinformatik, Fachhochschule Nordwestschweiz, Olten, Switzerland

    Thomas Hanne

  4. Department of Computer Science and information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Federal University of Bahia, Ondina, Brazil

    Tatiane Nogueira Rios

  6. Nantong University, Nantong Shi, Jiangsu, China

    Weiping Ding

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Issa, S., Khaled, A.R. (2022). Lower Limb Movement Recognition Using EMG Signals. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_31

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