Lower limb movement intention recognition for rehabilitation robot aided with projected recurrent neural network

doi:10.1007/s40747-021-00341-w

	Lower limb movement intention recognition for rehabilitation robot aided with projected recurrent neural network
	Liu, Mei 1,2; Peng, Bo 1,2; Shang, Mingsheng 1,2
刊名	COMPLEX & INTELLIGENT SYSTEMS
	2021-03-30
页码	12
关键词	Projected recurrent neural network (PRNN) Lower limb movement Intention recognition
ISSN号	2199-4536
DOI	10.1007/s40747-021-00341-w
通讯作者	Liu, Mei(liumeisysu@qq.com)
英文摘要	For the lower limb rehabilitation robot, how to better realize intention recognition is the key issue in the practical application. Recognition of the patient's movement intention is a challenging research work, which needs to be studied from the shallow to the deep. Specifically, it is necessary to ensure that the movement intention of the normal person can be accurately recognized, and then improve the model to realize the recognition of the movement intention of the patients. Therefore, before studying the patient's movement intention, it is essential to consider the normal person first, which is also for safety considerations. In recent years, a new Hill-based muscle model has been demonstrated to be capable of directly estimating the joint angle intention in an open-loop form. On this basis, by introducing a recurrent neural network (RNN), the whole prediction process can achieve more accuracy in a closed-loop form. However, for the traditional RNN algorithms, the activation function must be convex, which brings some limitations to the solution of practical problems. Especially, when the convergence speed of the traditional RNN model is limited in the practical applications, as the error continues to decrease, the convergence performance of the traditional RNN model will be greatly affected. To this end, a projected recurrent neural network (PRNN) model is proposed, which relaxes the condition of the convex function and can be used in the saturation constraint case. In addition, the corresponding theoretical proof is given, and the PRNN method with saturation constraint has been successfully applied in the experiment of intention recognition of lower limb movement compared with the traditional RNN model.
资助项目	Natural Science Foundation of Chongqing (China)[cstc2020jcyj-zdxmX0028] ; CAS Light of West China Program ; Chongqing Science and Technology Bureau[cstc2018jszx-cyzdX0041]
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER HEIDELBERG
WOS记录号	WOS:000635145200003
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/13338]
专题	中国科学院重庆绿色智能技术研究院
通讯作者	Liu, Mei
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
推荐引用方式 GB/T 7714	Liu, Mei,Peng, Bo,Shang, Mingsheng. Lower limb movement intention recognition for rehabilitation robot aided with projected recurrent neural network[J]. COMPLEX & INTELLIGENT SYSTEMS,2021:12.
APA	Liu, Mei,Peng, Bo,&Shang, Mingsheng.(2021).Lower limb movement intention recognition for rehabilitation robot aided with projected recurrent neural network.COMPLEX & INTELLIGENT SYSTEMS,12.
MLA	Liu, Mei,et al."Lower limb movement intention recognition for rehabilitation robot aided with projected recurrent neural network".COMPLEX & INTELLIGENT SYSTEMS (2021):12.