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下肢截肢者行走意图识别方法研究进展
引用本文:王蕾,王辉,黄品高,林闯,郑悦,魏月,郭欣,李光林.下肢截肢者行走意图识别方法研究进展[J].自动化学报,2018,44(8):1370-1380.
作者姓名:王蕾  王辉  黄品高  林闯  郑悦  魏月  郭欣  李光林
作者单位:1.中国科学院深圳先进技术研究院神经工程研究中心 深圳 518055
基金项目:国家自然科学基金61603375国家自然科学基金U1613222河北省青年自然科学基金F2016202327广东省基础与应用基础项目2014A020212383广东省基础与应用基础项目2014A020212046深圳市知识创新计划基础研究项目JCYJ20150402152130181
摘    要:直立行走是人类独立生活和正常参与社会活动的基本功能之一.人因遭受工伤、交通事故、战争、自然灾害(地震等)、疾病(糖尿病、癌症等)、先天出生缺陷等意外和不幸造成下肢截肢,从而部分或全部丧失行走能力,严重影响正常生活和参与社会活动.下肢假肢是下肢截肢者恢复行走功能的唯一手段,其技术发展吸引了众多研究者的关注.为使下肢假肢使用者能像正常腿一样或接近的步态行走,关键是实现截肢者行走意图的自动精确识别.本文首先探索了行走意图识别的内涵;然后从信号源的角度分析了不同截肢者行走意图识别方法的特点,尤其是神经功能重建作为补充的肌电信号(Electromyography,EMG)源的方法,并简述其研究进展,提出了一种融合生物力学信号和生物电信号的截肢者行走意图识别方法;最后对下肢截肢者行走意图识别方法发展趋势进行了总结和展望.

关 键 词:下肢假肢    行走意图识别    肌电信号    生物力学信号    目标肌肉神经分布重建
收稿时间:2017-05-12

Progress and Perspective of Recognition Methods for Walking Intention of Lower-limb Amputees
Affiliation:1.Research Center for Neural Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 5180552.School of Control Science and Engineering, Hebei University of Technology, Tianjin 3001303.Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055
Abstract:Walking is one of the basic functions of human beings to independently live and normally participate in social activities, thus recovery of walking function after lower-limb amputation would be significantly meaningful. Lower-limb prosthesis is a way to recover the walking ability, and it is the major substitution for the lost lower limb. Recently, the development of locomotion intention recognition for lower limb amputees has aroused the interest of many researchers. To achieve the goal of natural walking for amputees with lower limb prosthesis, the key point is to accurately and automatically identify their walking intentions. In this paper, we firstly describe the connotation and extension of locomotion intension. Then, we analyze the processes of different methods for locomotion intension recognition with several signal sources, especially hybrid reinnervation of targeted nerves and muscles as additional electromyography signal source. Finally, the methods with fusion signals by bio-mechanical signals and bioelectricity signals are proposed for walking intention recognition. In addition, challenges and future directions of locomotion intention recognition methods are also discussed.
Keywords:
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