基于肌力信号与电刺激感觉反馈的多自由度机械手人机交互控制  

Human-Machine Interaction Control Based on Force Myograph and Electrical Stimulation Sensory Feedback for Multi-DOF Robotic Hand

作　　者：李楠[1] 刘波[1] 霍宏[1] 叶玉璇[1] 姜力 

机构地区：[1]北京航天自动控制研究所,北京100854 [2]机器人技术与系统国家重点实验室,黑龙江哈尔滨150086

出　　处：《机器人》2015年第6期718-724,共7页Robot

基　　金：国家973计划资助项目(2011CB013306);国家自然科学基金资助项目(51175106)

摘　　要：为使操作者能够灵活控制多自由度机械手并能感受到机械手的抓取力,提出了一种具有双向信息传输能力的可穿戴式人机交互系统及控制方法.该系统利用压力传感器(FSR)阵列采集与操作者手部动作对应的前臂肌力信号,基于SVM(支持向量机)多类分类器算法实现对手部动作的识别,通过发送动作模式码控制机械手动作.另外,基于经皮神经电刺激(TENS)原理,将机械手抓取力信号转变为电刺激信号刺激体表皮肤,实现机械手抓握力向人体的感觉反馈.实验表明,基于肌力信号和SVM分类器的动作模式识别方法可实现对10种手部动作的识别,成功率不低于95%;电刺激感觉反馈可向人体准确反馈抓取力感并实现盲抓取.A wearable bi-directional human-machine interaction （HMI） system and its control methods are proposed to enable the user to control multi-DOF robotic hand freely and feel the gripping force from the robotic hand. A force sensory resistor （FSR） array is built to measure the forearm force myographic （FMG） signals corresponding to different hand motions of the user. A multiclass classifier is designed based on the support vector machine （SVM） algorithm to recognize the hand motions and generate motion codes to control the robotic hand movements. Moreover, sensory feedback is achieved by transforming the gripping force signals of the robotic hand into electrical stimulation signals of skin based on the principle of transcutaneous electrical nerve stimulation （TENS）. Experimental results show that the motion mode recognition method based on FMG and SVM can identify 10 typical hand motions with the accuracy of above 95%. The electrical stimulation method can feed back the perception of gripping force to the body accurately and help the user to grip objects without vision.

关 键 词：人机交互 机械手 肌力信号 电刺激 感觉反馈 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]