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Lower limb movement intention recognition for rehabilitation robot aided with projected recurrent neural network
Liu, Mei1,2; Peng, Bo1,2; Shang, Mingsheng1,2
2021-03-30
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.
关键词Projected recurrent neural network (PRNN) Lower limb movement Intention recognition
DOI10.1007/s40747-021-00341-w
发表期刊COMPLEX & INTELLIGENT SYSTEMS
ISSN2199-4536
页码12
通讯作者Liu, Mei(liumeisysu@qq.com)
收录类别SCI
WOS记录号WOS:000635145200003
语种英语