基于表面肌电信号的手指按键动作识别研究  

Research on Finger Key-press Gesture Recognition Based on Surface Electromyographic Signals

作　　者：成娟[1] 陈香[1] 路知远[1] 张旭[1] 赵章琰[1] 

机构地区：[1]中国科学技术大学电子科学与技术系,合肥230027

出　　处：《生物医学工程学杂志》2011年第2期352-356,370,共6页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(60703069);国家863计划资助项目(2009AA01Z322)

摘　　要：本文采用四通道表面肌电(SEMG)电极采集前臂动作肌电信号,对右手5个手指共16类按键动作进行了分类识别研究。研究内容包括按键动作的定义和数据采集方案设计,并通过两种数据处理分类实验对手指按键动作肌电信号分类识别的可行性及可重复性等问题进行探索。对6位受试者的实验结果显示,16类手指按键动作的单天平均识别率可达75.8%,且当训练样本数据增加到5 d时,多天数据分类准确率逼近单天分类结果,此结果验证了基于肌电信号的手指按键动作识别的可行性和可重复性。本文工作成果对基于肌电控制的虚拟键盘的交互实现有着重要的指导意义。This article reported researches on the pattern recognition of finger key-press gestures based on surface electromyographic（SEMG） signals.All the gestures were defined referring to the PC standard keyboard, and totally 16 sorts of key-press gestures relating to the right hand were defined.The SEMG signals were collected from the forearm of the subjects by 4 sensors.And two kinds of pattern recognition experiments were designed and implemented for exploring the feasibility and repeatability of the key-press gesture recognition based on SEMG signals.The results from 6 subjects showed,by using the same-day templates,that the average classification rates of 16 defined key-press gestures reached above 75.8%.Moreover,when the training samples added up to 5 days,the recognition accuracies approached those obtained with the same-day templates.The experimental results confirm the feasibility and repeatability of SEMG-based key-press gestures classification,which is meaningful for the implementation of myoelectric control-based virtual keyboard interaction.

关 键 词：表面肌电信号 人机交互 虚拟键盘 手指按键动作 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]