电子学报 ›› 2021, Vol. 49 ›› Issue (10): 1993-2001.DOI: 10.12263/DZXB.20200033

• 学术论文 • 上一篇    下一篇

基于Fisher判别和GKF-RELM算法的多特征步态模式识别

黎毅达1,2, 高发荣1, 姚婷1, 蔡利杰1   

  1. 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
  • 收稿日期:2019-12-25 修回日期:2020-12-17 出版日期:2021-10-25 发布日期:2021-10-25
  • 作者简介:黎毅达 男,1995年5月出生,湖南冷水江人.2020年毕业于杭州电子科技大学控制工程专业,获工学硕士学位.现就职于京东物流X研究部.主要研究方向为智能信息处理和自动驾驶视觉定位技术. E-mail: lyda2019@163.com
    高发荣(通信作者) 男,副教授,硕士生导师.2007年毕业于华中科技大学,获工学博士学位.现为杭州电子科技大学自动化学院教师.主要研究方向为机器人技术、智能信息处理和模式识别. E-mail: frgao@hdu.edu.cn
  • 基金资助:
    浙江省自然科学基金(LY20E050011)

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

Yi-da LI1,2, Fa-rong GAO1, Ting YAO1, Li-jie CAI1   

  1. 1.School of Automation,Hangzhou Dianzi University,Hangzhou,Zhejiang 310018,China
    2.Beijing Jingdong Dry Stone Technology Co. Ltd,Beijing 100176,China
  • Received:2019-12-25 Revised:2020-12-17 Online:2021-10-25 Published:2021-10-25

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.

关键词: 步态识别, 多特征融合, GKF-RELM算法, 深度神经网络, Fisher判别分析

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.

Key words: gait recognition, multi-feature fusion, GKF-RELM algorithm, deep neural network (DNN), Fisher discriminant analysis

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