sEMG-based continuous estimation of joint angles of human legs by using BP neural network

	sEMG-based continuous estimation of joint angles of human legs by using BP neural network
	Zhang, Feng1 ; Li, Pengfeng 1; Hou, Zeng-Guang1 ; Lu, Zhen 2; Chen, Yixiong 1; Li, Qingling 1; Tan, Min1
发表期刊	NEUROCOMPUTING
	2012-02-15
卷号	78 期号:1 页码:139-148
文章类型	Article
摘要	In this paper, we propose an mth order nonlinear model to describe the relationship between the surface electromyography (sEMG) signals and the joint angles of human legs, in which a simple BP neural network is built for the model estimation. The inputs of the model are sEMG time series that have been processed, and the outputs of the model are the joint angles of hip, knee, and ankle. To validate the effectiveness of the BP neural network, six able-bodied people and four spinal cord injury (SCI) patients participated in the experiment. Two movement modes including the treadmill exercise and the leg extension exercise at different speeds and different loads were respectively conducted by the able-bodied individuals, and only the treadmill exercise was selected for the SCI patients. Seven channels of sEMG from seven human leg muscles were recorded and three joint angles including the hip joint, knee joint and the ankle joint were sampled simultaneously. The results present that this method has a good performance on joint angles estimation by using sEMG for both able-bodied subjects and SCI patients. The average angle estimation root-mean-square (rms) error for leg extension exercise is less than 9 degrees, and the average rms error for treadmill exercise is less than 6 degrees for all the able-bodied subjects. The average angle estimation rms error of the SCI patients is even smaller (less than 5 degrees) than that of the able-bodied people because of a smaller movement range. This method would be used to rehabilitation robot or functional electrical stimulation (FES) for active rehabilitation of SCI patients or stroke patients based on sEMG signals. (C) 2011 Elsevier B.V. All rights reserved.
关键词	Semg Rehabilitation Bp Sci
WOS标题词	Science & Technology ; Technology
关键词[WOS]	SURFACE EMG ; SIGNAL BANDWIDTH ; MUSCLE ; MODEL ; ROBOT ; CLASSIFICATION ; ARM
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000298528200017
引用统计	被引频次：131[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3513
专题	复杂系统认知与决策实验室_先进机器人
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing 100190, Peoples R China 2.China Rehabil Res Ctr, Dept SCI Surg, Beijing 100068, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zhang, Feng,Li, Pengfeng,Hou, Zeng-Guang,et al. sEMG-based continuous estimation of joint angles of human legs by using BP neural network[J]. NEUROCOMPUTING,2012,78(1):139-148.
APA	Zhang, Feng.,Li, Pengfeng.,Hou, Zeng-Guang.,Lu, Zhen.,Chen, Yixiong.,...&Tan, Min.(2012).sEMG-based continuous estimation of joint angles of human legs by using BP neural network.NEUROCOMPUTING,78(1),139-148.
MLA	Zhang, Feng,et al."sEMG-based continuous estimation of joint angles of human legs by using BP neural network".NEUROCOMPUTING 78.1(2012):139-148.

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