Measurement, Evaluation, and Control of Active Intelligent Gait Training Systems—Analysis of the Current State of the Art

Electronics

Shuoyu Wang¹  João P. Ferreira²  Xiufeng Zhang³  Ning Zhang³  Yi Han⁴  Chenhao Liu⁴  Tao Liu⁴  Bin Zhang⁴  Meimei Han⁵ 

[1] Department of Intelligent Mechanical Systems Engineering, Kochi University of Technology 185 Miyanokuchi, Tosayamada-cho, Kami-city 782-8502, Japan;Institute of Superior of Engineering of Coimbra, Quinta da Nora, 3030-199 Coimbra, Portugal;Key Laboratory of Rehabilitation Technical Aids Technology and System of the Ministry of Civil Affairs, National Research Center for Rehabilitation Technical Aids, Beijing 100176, China;State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China;Zhejiang Fuzhi Science and Technology Innovation Co., Ltd., Hangzhou 310027, China;

关键词: rehabilitation and assistance system;    lower limbs;    intention recognition;    gait training;    gait evaluation;    human–machine interaction control strategy;   

DOI  :  10.3390/electronics11101633

来源: DOAJ

【 摘 要 】

Gait recognition and rehabilitation has been a research hotspot in recent years due to its importance to medical care and elderly care. Active intelligent rehabilitation and assistance systems for lower limbs integrates mechanical design, sensing technology, intelligent control, and robotics technology, and is one of the effective ways to resolve the above problems. In this review, crucial technologies and typical prototypes of active intelligent rehabilitation and assistance systems for gait training are introduced. The limitations, challenges, and future directions in terms of gait measurement and intention recognition, gait rehabilitation evaluation, and gait training control strategies are discussed. To address the core problems of the sensing, evaluation and control technology of the active intelligent gait training systems, the possible future research directions are proposed. Firstly, different sensing methods need to be proposed for the decoding of human movement intention. Secondly, the human walking ability evaluation models will be developed by integrating the clinical knowledge and lower limb movement data. Lastly, the personalized gait training strategy for collaborative control of human–machine systems needs to be implemented in the clinical applications.

【 授权许可】

Unknown