Model for predicting the angles of upper limb joints in combination with sEMG and posture capture

Author:

Wang Zhen-YuORCID,Xiang Ze-RuiORCID,Zhi Jin-Yi,Ding Tie-Cheng,Zou Rui,Lan Yong-Xia

Abstract

Abstract Since poor man–machine interaction and insufficient coupling occur in the processes of angle prediction and rehabilitation training based purely on the surface electromyography (sEMG) signal, a model for predicting the angles of upper limb joints was presented and validated by experiments. The sEMG and posture capture features were combined to build a hybrid vector, and the intentions of upper limb movements were characterized. The original signals were pre-treated with debiasing, filtering, and noise reduction, and then they were integrated to obtain signal characteristics. Then, feature values in the time domain, frequency domain, time-frequency domain, and entropy were extracted from the treated signals. The snake optimizer least squares support vector machine (SO-LSSVM) was modeled to predict the angles of upper limb joints to improve the poor precision and slow velocity of existing models in the movement control field. Experimental results showed that the prediction model performed well in predicting the motion trails of human upper limb joints from the sEMG signal and attitude information. It effectively reduced both skewing and error in prediction. Hence, it holds great promise for improving the man–machine coupling precision and velocity. Compared to the conventional LSSVM model, the proposed SO-LSSVM model reduced the training time, execution time, and root mean square error of evaluation parameters by 65%, 11%, and 76%, respectively. In summary, the proposed SO-LSSVM model satisfied the real-time requirement for rehabilitation robots and showed high accuracy and robustness.

Funder

The 2th Batch of 2022 MOE of PRC Industry-University Collaborative Education Program

New interdisciplinary Cultivation Fund Program of Southwest Jiaotong University

Publisher

IOP Publishing

Subject

Applied Mathematics,Instrumentation,Engineering (miscellaneous)

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3