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Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms.

Farong Gao1, Taixing Tian1, Ting Yao1

  • 1School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China.

Computational Intelligence and Neuroscience
|March 17, 2021
PubMed
Summary
This summary is machine-generated.

This study enhances human gait recognition using an optimized Support Vector Machine (SVM) with an artificial bee colony (ABC-SVM). The improved algorithm boosts recognition accuracy by combining multiple surface electromyography (sEMG) signal features.

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Human gait recognition is crucial for various applications.
  • Surface electromyography (sEMG) signals offer rich information for gait analysis.
  • Existing gait recognition methods can be improved for higher accuracy and stability.

Purpose of the Study:

  • To develop an improved gait recognition algorithm using optimized Support Vector Machine (SVM) with Artificial Bee Colony (ABC).
  • To explore the effectiveness of combining multiple features from sEMG signals.
  • To enhance the accuracy and stability of human gait recognition.

Main Methods:

  • Extracted four types of features from denoised sEMG signals: time-domain (IAV, VAR, ZC), frequency-domain (MPF, MF), wavelet, and fuzzy entropy.
  • Employed SVM, Linear Discriminant Analysis (LDA), and Extreme Learning Machine (ELM) classifiers.
  • Optimized SVM parameters (penalty coefficient, kernel function) using the ABC algorithm.

Main Results:

  • The ABC-SVM classifier significantly outperformed the non-optimized SVM, increasing average recognition rate by 3.18%.
  • Combined feature samples (time-domain, frequency-domain, wavelet, fuzzy entropy) improved gait classification accuracy.
  • Combined features also enhanced the overall recognition stability.

Conclusions:

  • The proposed ABC-SVM algorithm offers a significant improvement in human gait recognition accuracy and stability.
  • Combining diverse sEMG features is effective for robust gait analysis.
  • The ABC optimization method is valuable for enhancing machine learning model performance in gait recognition.