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

针对肌电交互系统中因电极断开、损坏及数据传输中断等故障造成的数据错误/丢失问题,提出一种基于高斯混合模型的肌电信号容错分类方法,通过对肌电信号特征样本中错误/丢失数据边缘化或条件均值归错实现非完整数据样本分类.应用所提出的方法识别5种手部动作,实验结果表明,该方法的动作识别精度要高于传统的零归错与均值归错方法.最后,融合容错分类机制开发了肌电假手平台,在线实验验证了提出的方法可以有效提高肌电交互系统的鲁棒性.

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.