Progress and Perspective of Recognition Methods for Walking Intention of Lower-limb Amputees
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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.
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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
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.1) 本文责任编委 魏庆来 -
图 1 基于不同信息源的下肢截肢者行走意图识别
Fig. 1 Lower-limb locomotion intent recognition based on several signal sources
图 2 基于生物力学信息的截肢者行走意图识别原理示意图
Fig. 2 Lower-limb locomotion intent recognition based on biomechanical signals
图 3 基于肌电信息识别截肢者行走意图原理示意图
Fig. 3 Lower-limb locomotion intent recognition based on sEMG signals
图 5 利用TMR技术重建截肢者的运动神经功能
Fig. 5 The neurological redirection to innervate accessory muscles by TMR
图 6 基于神经机器接口的截肢者行走意图识别示意图
Fig. 6 Lower-limb locomotion intent recognition based on different neural-machine interfaces
图 7 基于生物力学信息和肌电信息的多信息源融合的截肢者行走意图识别原理
Fig. 7 Lower-limb locomotion intent recognition based on biomechanical signals and sEMG signals
表 1 人体下肢行走意图识别的内涵
Table 1 The connotation and extension of locomotion intension of lower-limb human body
行走意图分类(功能) 行走模式(核心) 相位周期(基础) 步态事件(前提) 下肢具体动作 步行
上、下楼梯
上、下斜坡站立弯曲相
站立伸展相
摆动弯曲相
摆动伸展相脚跟触地(HS)
全足着地(FF)
足跟离地(HF)
脚尖离地(TO)
脚尖触地(TS)
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表 2 利用不同信息源进行行走意图识别的性能比较
Table 2 A comparison with several methods based on different signal sources
信号分类 生物力学信号 肌电信号 运动单元动作电位序列 目标肌肉神经分布重建混合接口 脑电信号 优点 主要信号来源; 行走模式识别准确率高; 无创伤, 安全使用方便 较直接的信号来源; 可以检测肌肉力的变化; sEMG行走模式切换自然; 无创伤, 安全, 使用方便 最直接的信号来源; 更加准确地估计肌肉力, 动作类型识别精确, 无创伤, 安全 提供附加神经信息, 丰富信号来源; 无创伤, 安全, 使用方便 获取更多的假肢控制信息 缺点 无法检测神经肌肉状态 PNS植入式电极有创、在体内易纤维化造成测量失效; EMG信号源依赖患者截肢程度; sEMG具有时变特性, 易受环境干扰 记录的数据复杂而庞大, 计算困难 手术方案设计复杂, 对临床经验的要求高 可检测的神经信号微弱 应用场景 产品化 处于临床阶段 实验室分析 临床 实验室分析
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