Multi-Feature Gait Pattern Recognition Based on Fisher Discriminant and GKF-RELM Algorithm

LI Yi-da; GAO Fa-rong; YAO Ting; CAI Li-jie

doi:10.12263/DZXB.20200033

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Multi-Feature Gait Pattern Recognition Based on Fisher Discriminant and GKF-RELM Algorithm

PAPERS | 更新时间：2025-12-08

- Multi-Feature Gait Pattern Recognition Based on Fisher Discriminant and GKF-RELM Algorithm
- ACTA ELECTRONICA SINICA Vol. 49, Issue 10, Pages: 1993-2001(2021)
- 作者机构：
  
  1.杭州电子科技大学自动化学院，浙江杭州 310018
  2.北京京东乾石科技有限公司，北京 100176
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20200033
  CLC： TP391;
- Received：25 December 2019，
  
  Revised：2020-12-17，
  
  Published：25 October 2021
- 稿件说明：
移动端阅览
黎毅达,高发荣,姚婷等.基于Fisher判别和GKF-RELM算法的多特征步态模式识别[J].电子学报,2021,49(10):1993-2001.

LI Yi-da,GAO Fa-rong,YAO Ting,et al.Multi-Feature Gait Pattern Recognition Based on Fisher Discriminant and GKF-RELM Algorithm[J].ACTA ELECTRONICA SINICA,2021,49(10):1993-2001.
黎毅达,高发荣,姚婷等.基于Fisher判别和GKF-RELM算法的多特征步态模式识别[J].电子学报,2021,49(10):1993-2001. DOI： 10.12263/DZXB.20200033.

LI Yi-da,GAO Fa-rong,YAO Ting,et al.Multi-Feature Gait Pattern Recognition Based on Fisher Discriminant and GKF-RELM Algorithm[J].ACTA ELECTRONICA SINICA,2021,49(10):1993-2001. DOI： 10.12263/DZXB.20200033.

摘要

为提高下肢表面肌电信号步态识别的识别精度和计算效率，采用一种基于高斯核函数优化正则化超限学习机（GKF-RELM）算法，对肌电信号提取时域、频域和非线性动力学三类特征并分别计算步态识别率，运用Fisher判别函数分析所提特征的可分性，得到多类特征的融合特征作为输入数据对分类器进行训练，再用训练好的分类器进行步态识别，从识别率和计算时间两方面，分别与支持向量机（SVM）和深度神经网络（DNN）方法进行了对比分析.结果表明，基于Fisher判别可分性指标确定的多类特征组合，能得到最优识别效果，并在提高分类精度的同时，优化了计算效率.此外，GKF-RELM方法的识别率也优于传统的ELM方法.

Abstract

To improve the recognition accuracy and computational efficiency of gait recognition for lower extremity surface electromyography (sEMG)

the Gaussian kernel function-regularized extreme learning machine (GKF-RELM) algorithm is presented. The features of time domain

frequency domain and non-linear dynamics via sEMG signals are extracted and the corresponding gait recognition rates are calculated

respectively. Fisher discriminant function is utilized to analyze the separability of the proposed features

and the fusion features of multi-class features are obtained as the input data to train the classifiers

and the trained classifier is used for gait recognition. The recognition rate and calculation time are compared with support vector machine (SVM) and deep neural network (DNN). The results show that the combination of multi-class features based on Fisher discriminant separability index can obtain the optimal recognition effects

and improve the classification accuracy

as well as optimize the calculation efficiency. In addition

the recognition rate of GKF-RELM method is preferable to that of traditional ELM method.

关键词

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references

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