基于预抓取模式识别的假手肌电控制方法  

Recognition of Hand Grasp Preshaping Patterns Applied to Prosthetic Hand Electromyography Control

作　　者：杨大鹏[1] 赵京东[1] 李楠[1] 姜力[1] 刘宏[1] 

机构地区：[1]哈尔滨工业大学机器人技术与系统国家重点实验室,哈尔滨150001

出　　处：《机械工程学报》2012年第15期1-8,共8页Journal of Mechanical Engineering

基　　金：国家重点基础研究发展计划(973计划;2011CB013306;2011CB013305);国家自然科学基金(51175106;60775060);机器人技术与系统国家重点实验室自主研究课题(SKLRS201201A03)资助项目

摘　　要：为解决多自由度假手肌电控制难题,建立一种人手预抓取模式的在线识别方法,并将其应用至HIT-DLR假手的抓取控制。基于Teager-Kaiser能量算子增幅肌电信号在肌肉动作发起时的变化,引入后处理解决噪声影响,提出一种预抓取发起的在线检测方法。针对人手4种预抓取模式,讨论不同肌电信号分段方法,不同时域特征、频域特征和时频域特征以及多种分类方法所能获得的识别成功率。最终建立了基于波形长度及支持矢量机的最优识别方法,成功率可达95%,延迟小于300 ms。肌电控制试验表明,假手可以准确快速地抓取各种不同形状的物体。It appears a big challenge when the multi-DOFs prosthetic hand is controlled by the electromyography （EMG） signals. A novel recognition method of the hand grasp preshaping patterns is proposed to the HIT-DLR prosthetic hand＇s EMG control. A new online detection method is designed to collect the accurate onset EMG signals of the grasp preshaping, which uses the Teager-Kaiser energy （TKE） operator and post processing to enlarge the changes of the EMG signal and deal with the spike noise, respectively. Focusing on 4 types of the hand preshaping patterns, different data segmentation methods, different features coming from the time-domain, frequency domain and time-frequency domain, and various classifiers are attempted to find the best classification accuracy. The waveform length and support vector machine are chosen, which can reach an accuracy of 95% and a response time less than 300 ms. The experiment of the prosthetic hand control shows that the hand can swiftly grasp the objects with various shapes.

关 键 词：假手 肌电控制 预抓取 模式识别 

分 类 号：R241[医药卫生—中医诊断学]