嵌入式人手姿态肌电模式在线识别方法

为了实现多自由度假手的肌电控制,需要嵌入式地实现先进模式识别方法.分别采用K近邻法及支持向量机分类方法,在样本充足以及相对匮乏的情况下,对实验中采集肌电信号的阈值特征集和稳态特征集进行了模式识别操作.实验结果表明,支持向量机的方法要明显优于近邻法,采用阈值数据作为训练样本要比稳态数据实时识别效果好.给出了一种在DSP内基于支持向量机进行10种人手姿态肌电模式的在线识别方法,识别率在95%以上,决策频率约为30Hz.

Embeded online recognition of hand gesture EMG modes

Controlling a multi-DOF prosthetic hand by EMG signals demands for effective pattern recognition methods that can be easily embedded in the controller of the hand.In this paper,methods of K-nearest neighbor and support vector machine(SVM) were used to identify different modes of myoelectric signals,which were obtained in several on-line experiments.Both methods were performed on different training sample sets,called threshold set and steady-state set,and in the case of abundance and relative insufficiency of samples.Experimental results show that the SVM method is superior to K-nearest neighbor,and the real-time recognition results are better when using threshold dataset as training samples than using steady-state dataset.The proposed method,which is based on SVM and embedded in DSP,can discriminate 10 hand gesture EMG modes with a prediction accuracy of above 95% and a decision frequency of about 30 Hz.