[1]张建华,王豪,李克祥,等.基于KPCA的膝关节多模式连续运动估计[J].中国医学物理学杂志,2023,40(6):742-749.[doi:DOI:10.3969/j.issn.1005-202X.2023.06.012]
　ZHANG Jianhua,WANG Hao,LI Kexiang,et al.KPCA-based continuous motion estimation of knee joint in multiple motion modes[J].Chinese Journal of Medical Physics,2023,40(6):742-749.[doi:DOI:10.3969/j.issn.1005-202X.2023.06.012]
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基于KPCA的膝关节多模式连续运动估计()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第6期
页码:
742-749
栏目:
医学信号处理与医学仪器
出版日期:
2023-06-27

文章信息/Info

Title:
KPCA-based continuous motion estimation of knee joint in multiple motion modes
文章编号:
1005-202X（2023）06-0742-08
作者:
张建华1; 王豪1; 李克祥1; 2; 王唱1; 2
1.河北工业大学机械工程学院， 天津 300401； 2.河北工业大学电工装备可靠性与智能化国家重点实验室， 天津 300401
Author(s):
ZHANG Jianhua1;  WANG Hao1;  LI Kexiang1;  2;  WANG Chang1;  2
1. School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China 2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China
关键词:
表面肌电信号; 核主成分分析; 时域特征; 连续运动估计
Keywords:
Keywords: surface electromyography signal kernel principal component analysis time domain feature continuous motion estimation
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2023.06.012
文献标志码:
A
摘要:
为了实现不同运动模式下膝关节连续运动的有效估计，提出一种基于核主成分分析（KPCA）的下肢膝关节连续运动估计方法。首先，融合多维表面肌电信号时域特征获取不同运动模式下较为全面的运动信息；其次，采用KPCA方法进行肌电特征降维，获取与该类运动模式最为相关的主成分向量，并基于反向传播神经网络实现不同运动模式下膝关节连续运动的有效估计；最后，对5个实验对象的4种运动模式进行实验验证。结果表明该方法不仅可有效估计不同运动模式下膝关节连续运动角度，相对于PCA算法估计精度也有明显提高。
Abstract:
Abstract: A kernel principal component analysis (KPCA) based method is proposed for the effective estimation of multi-mode continuous motion of knee joint. The time domain features of multi-dimensional surface electromyography signal are fused to obtain the comprehensive motion information in different motion modes. Then, KPCA method is used to reduce the dimensionality of EMG features and obtain the most relevant principal component vectors, and the effective estimation of multi-mode continuous motion of knee joint is realized through the combination with back propagation neural network. Experimental verification is carried out on 4 motion modes of 5 subjects. The results show that the proposed method can not only effectively estimate the multi-mode continuous motion angles of knee joint, but also significantly improve the estimation accuracy as compared with PCA algorithm.

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备注/Memo

备注/Memo:
【收稿日期】2023-01-04 【基金项目】河北省自然科学基金（F20202051）；河北省博士后科研项目（B2022003016）；省部共建电工装备可靠性与智能化国家重点实验室人才培育项目（EERIPD2021011）；天津市杰出青年科学基金（19JCJQJC61600） 【作者简介】张建华，博士，教授，研究方向：康复机器人、智能机器人技术、模式识别，E-mail: jhzhang@hebut.edu.cn 【通信作者】李克祥，博士，讲师，研究方向：外骨骼机器人、人体意图识别、人机交互，E-mail: kexiang_lee@163.com
更新日期/Last Update: 2023-06-28