Prediction of Long-Term Elbow Flexion Force Intervals Based on the Informer Model and Electromyography

doi:10.3390/electronics10161946

	Prediction of Long-Term Elbow Flexion Force Intervals Based on the Informer Model and Electromyography
	Lu, Wei 1,2; Gao, Lifu 1; Li, Zebin 1,2; Wang, Daqing 1; Cao, Huibin 1
刊名	ELECTRONICS
	2021-08-01
卷号	10
关键词	electromyography Informer force prediction long-term prediction confidence intervals
DOI	10.3390/electronics10161946
通讯作者	Li, Zebin(ba20168195@mail.ustc.edu.cn) ; Cao, Huibin(hbcao@iim.ac.cn)
英文摘要	Accurate and long-term prediction of elbow flexion force can be used to recognize the intended movement and help wearable power-assisted robots to improve control performance. Our study aimed to find a proper relationship between electromyography and flexion force. However, the existing methods must incorporate biomechanical models to produce accurate and timely predictions of flexion force. Elbow flexion force is largely determined by the contractile properties of muscles, and the relationship between flexion force and the motor function of muscles has to be thoroughly analyzed. Therefore, based on the investigation on the contributions of different muscles to the flexion force, original electromyography signals were decomposed into non-linear and non-stationary parts. We selected the mean absolute value (MAV) of the non-linear part and the variance of the non-stationary part as inputs for an Informer prediction model that does not require detailed a priori knowledge of biomechanical models and is optimized for processing time sequences. Finally, a long-term flexion force probability interval is proposed. The proposed framework performs well in predicting long-term flexion force and outperforms other state-of-the-art models when compared to experimental results.
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences[XDA22040303] ; Natural Science Foundation of Anhui Province[1808085QF514] ; National Natural Science Foundation of China[92067205] ; HFIPS Director's Fund[YZJJ2021QN25]
WOS研究方向	Computer Science ; Engineering ; Physics
语种	英语
出版者	MDPI
WOS记录号	WOS:000688894100001
资助机构	Strategic Priority Research Program of the Chinese Academy of Sciences ; Natural Science Foundation of Anhui Province ; National Natural Science Foundation of China ; HFIPS Director's Fund
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/125207]
专题	中国科学院合肥物质科学研究院
通讯作者	Li, Zebin; Cao, Huibin
作者单位	1.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Dept Sci Isl, Hefei 230026, Peoples R China
推荐引用方式 GB/T 7714	Lu, Wei,Gao, Lifu,Li, Zebin,et al. Prediction of Long-Term Elbow Flexion Force Intervals Based on the Informer Model and Electromyography[J]. ELECTRONICS,2021,10.
APA	Lu, Wei,Gao, Lifu,Li, Zebin,Wang, Daqing,&Cao, Huibin.(2021).Prediction of Long-Term Elbow Flexion Force Intervals Based on the Informer Model and Electromyography.ELECTRONICS,10.
MLA	Lu, Wei,et al."Prediction of Long-Term Elbow Flexion Force Intervals Based on the Informer Model and Electromyography".ELECTRONICS 10(2021).