机器人Issue(1)：9-16,8.DOI:10.13973/j.cnki.robot.2015.0009
基于肌电信号容错分类的手部动作识别
Recognizing Hand Motions Based on Fault-tolerant Classification with EMG Signals
摘要
Abstract
In view of the fault/missing data problem caused by disconnected/damaged electrodes and data-transmission in-terrupting in myoelectric-interface systems, an EMG (electromyography) fault-tolerant classification method based on Gaus-sian mixture model is proposed, with which an incomplete-data sample can be classified via marginalizing or conditional-mean imputation of the fault/missing data in the EMG feature sample. The proposed method is applied to recognizing five kinds of hand motion. Experimental results show that the proposed method can provide higher motion-recognition accuracy than that by the traditional zero and mean imputation methods. Finally, a myoelectric-hand platform is developed by involv-ing the fault-tolerant classification mechanism, and the online experiments show that the proposed method can effectively improve the robustness of myoelectric-interface systems.关键词
肌电信号/数据丢失/动作分类/人机交互Key words
EMG/data missing/motion classification/human-robot interface分类
信息技术与安全科学引用本文复制引用
丁其川,赵新刚,韩建达..基于肌电信号容错分类的手部动作识别[J].机器人,2015,(1):9-16,8.基金项目
国家自然科学基金资助项目（61273355，61273356，61035005）． （）
