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

Fang Peng, Wei Peng, Cheng Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationCognitive Systems and Signal Processing - 4th International Conference, ICCSIP 2018, Revised Selected Papers
EditorsDewen Hu, Fuchun Sun, Huaping Liu
PublisherSpringer-Verlag
Pages138-149
Number of pages12
ISBN (Print)9789811379857
DOIs
Publication statusPublished - 2019 Jan 1
Event4th International Conference on Cognitive Systems and Information Processing, ICCSIP 2018 - Beijing, China
Duration: 2018 Nov 292018 Dec 1

Publication series

NameCommunications in Computer and Information Science
Volume1006
ISSN (Print)1865-0929

Conference

Conference4th International Conference on Cognitive Systems and Information Processing, ICCSIP 2018
CountryChina
CityBeijing
Period18/11/2918/12/1

Keywords

  • Classifier
  • Features extraction
  • Gait phase detection
  • LightGBM
  • sEMG

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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  • Cite this

    Peng, F., Peng, W., & Zhang, C. (2019). Evaluation of sEMG-Based Feature Extraction and Effective Classification Method for Gait Phase Detection. In D. Hu, F. Sun, & H. Liu (Eds.), Cognitive Systems and Signal Processing - 4th International Conference, ICCSIP 2018, Revised Selected Papers (pp. 138-149). (Communications in Computer and Information Science; Vol. 1006). Springer-Verlag. https://doi.org/10.1007/978-981-13-7986-4_13