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Evaluation of sEMG-Based Feature Extraction and Effective Classification Method for Gait Phase Detection

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Cognitive Systems and Signal Processing (ICCSIP 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1006))

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Abstract

Gait phase detection is an essential procedure for amputated person with an artificial leg to walk naturally. However, a high-performance gait phase detection system is challenging due to (1) the complexity of surface electromyography (sEMG) and redundancy among the numerous features; (2) a robust recognition algorithm which can satisfy the real-time and high accuracy requirement of the system. This paper presents a gait phase detection method based on feature selection and ensemble learning. Four kinds of features extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are quantitatively analyzed by statistical analysis and calculation complexity to select the best features set. Furthermore, a multiclass classifier using Light Gradient Boosting Machine (LightGBM) is first introduced in gait recognition for discriminating six different gait phases with an average accuracy (94.1%) in a reasonable calculation time (85 ms), and the average accuracy is 5%, which is better than the traditional multiple classifiers decision fusion model. The proposed robust algorithm can effectively reduce the effect of speed on the result, which make it a perfect choice for gait phase detection.

This work was partially supported by Guangdong Science and Technology Plan Project under Grant 2016A020220003.

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Authors and Affiliations

  1. School of Automation Science and Engineering, South China University of Technology, Guangzhou, China

    Fang Peng

  2. Zhongshan Institute, University of Electronic Science and Technology, Zhongshan, China

    Fang Peng & Cheng Zhang

  3. University of Electronic Science and Technology, Chendu, China

    Wei Peng

  4. Department of Computer Science and Communications Engineering, Waseda University, Tokyo, Japan

    Cheng Zhang

Authors
  1. Fang Peng
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  2. Wei Peng
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  3. Cheng Zhang
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Correspondence to Cheng Zhang .

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Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Fuchun Sun

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Huaping Liu

  3. College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China

    Dewen Hu

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Peng, F., Peng, W., Zhang, C. (2019). Evaluation of sEMG-Based Feature Extraction and Effective Classification Method for Gait Phase Detection. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-7986-4_13

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  • DOI: https://doi.org/10.1007/978-981-13-7986-4_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7985-7

  • Online ISBN: 978-981-13-7986-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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