Continuous Estimation of Human Multi-joint Angles from sEMG Using a State-space Model

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	Continuous Estimation of Human Multi-joint Angles from sEMG Using a State-space Model
	Ding QC(丁其川); Han JD(韩建达); Zhao XG(赵新刚)
刊名	IEEE Transactions on Neural Systems and Rehabilitation Engineering
	2016
页码	1-11
关键词	Surface electromyography (sEMG) closed-loop estimation multi-joint movement redundancy segmentation
ISSN号	1534-4320
通讯作者	丁其川
产权排序	1
中文摘要	Due to the couplings among joint-relative muscles, it is a challenge to accurately estimate continuous multi-joint movements from multi-channel sEMG signals. Traditional approaches always build a nonlinear regression model, such as artificial neural network, to predict the multi-joint movement variables using sEMG as inputs. However, the redundant sEMGdata are always not distinguished; the prediction errors cannot be evaluated and corrected online as well. In this work, a correlation-based redundancy-segmentation method is proposed to segment the sEMG-vector including redundancy into irredundant and redundant subvectors. Then, a general state-space framework is developed to build the motion model by regarding the irredundant subvector as input and the redundant one as measurement output. With the built state-space motion model, a closed-loop prediction-correction algorithm, i.e., the unscented Kalman filter (UKF), can be employed to estimate the multijoint angles from sEMG, where the redundant sEMG-data are used to reject model uncertainties. After having fully employed the redundancy, the proposed method can provide accurate and smooth estimation results. Comprehensive experiments are conducted on the multi-joint movements of the upper limb. The maximum RMSE of the estimations obtained by the proposed method is 0.160.03, which is significantly less than 0.250.06 and 0.270.07 (p<0.05) obtained by common neural networks.
语种	英语
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/20237]
专题	沈阳自动化研究所_机器人学研究室
推荐引用方式 GB/T 7714	Ding QC,Han JD,Zhao XG. Continuous Estimation of Human Multi-joint Angles from sEMG Using a State-space Model[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2016:1-11.
APA	Ding QC,Han JD,&Zhao XG.(2016).Continuous Estimation of Human Multi-joint Angles from sEMG Using a State-space Model.IEEE Transactions on Neural Systems and Rehabilitation Engineering,1-11.
MLA	Ding QC,et al."Continuous Estimation of Human Multi-joint Angles from sEMG Using a State-space Model".IEEE Transactions on Neural Systems and Rehabilitation Engineering (2016):1-11.