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

Fang Peng, Wei Peng, Cheng Zhang*

*この研究の対応する著者

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルCognitive Systems and Signal Processing - 4th International Conference, ICCSIP 2018, Revised Selected Papers
編集者Dewen Hu, Fuchun Sun, Huaping Liu
出版社Springer-Verlag
ページ138-149
ページ数12
ISBN(印刷版)9789811379857
DOI
出版ステータスPublished - 2019 1 1
イベント4th International Conference on Cognitive Systems and Information Processing, ICCSIP 2018 - Beijing, China
継続期間: 2018 11 292018 12 1

出版物シリーズ

名前Communications in Computer and Information Science
1006
ISSN(印刷版)1865-0929

Conference

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

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 数学 (全般)

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