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利用肌电信号求解关节力矩的研究及应用综述
引用本文:姜峰1,尹逊锋2,衣淳植2,杨炽夫2. 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
作者姓名:姜峰1  尹逊锋2  衣淳植2  杨炽夫2
作者单位:1. 哈尔滨工业大学 计算机科学与技术学院, 黑龙江 哈尔滨 150001;2. 哈尔滨工业大学 机电工程学院, 黑龙江 哈尔滨 150001
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。

关 键 词:表面肌电信号  神经激活  信号处理  肌肉激活  肌肉骨骼模型  关节力矩  关节动力学  参数辨识

A review of the research and application of calculating joint torque by electromyography signals
JIANG Feng1,YIN Xunfeng2,YI Chunzhi2,YANG Chifu2. A review of the research and application of calculating joint torque by electromyography signals[J]. CAAL Transactions on Intelligent Systems, 2020, 15(2): 193-203. DOI: 10.11992/tis.202001013
Authors:JIANG Feng1  YIN Xunfeng2  YI Chunzhi2  YANG Chifu2
Affiliation:1. College of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China;2. School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
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.
Keywords:surface electromyography   neural activation   signal processing   muscle activation   musculoskeletal model   joint moment   joint dynamics   parameter identification
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