[1]姜峰,尹逊锋,衣淳植,等.利用肌电信号求解关节力矩的研究及应用综述[J].智能系统学报,2020,15(2):193-203.[doi:10.11992/tis.202001013]
JIANG Feng,YIN Xunfeng,YI Chunzhi,et al.A review of the research and application of calculating joint torque by electromyography signals[J].CAAI Transactions on Intelligent Systems,2020,15(2):193-203.[doi:10.11992/tis.202001013]
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JIANG Feng,YIN Xunfeng,YI Chunzhi,et al.A review of the research and application of calculating joint torque by electromyography signals[J].CAAI Transactions on Intelligent Systems,2020,15(2):193-203.[doi:10.11992/tis.202001013]
利用肌电信号求解关节力矩的研究及应用综述
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第2期
页码:
193-203
栏目:
综述
出版日期:
2020-03-05
- Title:
- A review of the research and application of calculating joint torque by electromyography signals
- Keywords:
- surface electromyography; neural activation; signal processing; muscle activation; musculoskeletal model; joint moment; joint dynamics; parameter identification
- 分类号:
- TP391.4
- 摘要:
- 表面肌电信号(surface electromyography, sEMG)是人体的易于检测的神经信号，其富含大量人体运动信息。利用肌电信号作为输入信号，结合相关生物学模型分析肌电信号同肌肉力和对应关节力矩之间的关系，对于深入理解分析人体动力学具有重要意义。本文详细归纳总结了利用肌电信号求解人体关节力矩方法的研究成果，同时介绍神经肌肉骨骼模型的计算及优化过程，给出部分模型生理参数为之后的研究提供参照，并给出现阶段该方法在人体关节力矩求解中的应用。通过分析该求解过程中所面临的一些问题，总结出该方法的发展展望，为之后的研究提供参考。
- Abstract:
- Surface electromyography is a neuronal signal of the human body that is easily detected. It extensively provides information about human motion. For a deeper understanding of human body dynamics, the use of electromyographic signals and biological models to examine the relationship between myoelectric signals and muscle forces or corresponding joint torques, is of great importance. This paper summarizes:the research results of solving human joint torque using electromyography signals; introduces the process of measuring and optimizing the neuromusculoskeletal model;gives some model physiological parameters to provide reference for future research; and provides application of the method in solving human joint torque in the current stage. Some of the problems encountered in the solution process are then evaluated, and the development prospect of the method is summarized, providing a reference for future research.
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备注/Memo
收稿日期:2020-01-08。
基金项目:国家重点研发计划项目(2018YFC0806800, 2018YFC0832105)
作者简介:姜峰，教授，IEEE哈尔滨信号处理分会主席、美国Princeton大学电子工程系访问学者、核九院特聘专家、黑龙江VR联盟首席专家，主要研究方向为计算机视觉、模式识别、视频图像处理、下肢外骨骼机器人。近5年主持国家自然科学基金面上项目、国家自然科学基金青年项目、军委科技委项目、国际合作项目等10余项；参与国家重点研发计划、国家自然科学基金重点项目、国家973计划、863计划、国际合作项目20项。获军队科技进步二等奖(排名第二)、黑龙江省高校科技奖一等奖。出版中文、英文专著和教材3部。发表学术论文100余篇;尹逊锋，硕士研究生，主要研究方向为下肢外骨骼机器人、人体运动学、生物电信号分析;衣淳植，博士研究生，主要研究方向为下肢外骨骼机器人、人机协作、人体运动学、惯性导航、仿生学
通讯作者:尹逊锋.E-mail:mr_yinxf@hit.edu.cn
更新日期/Last Update:
1900-01-01