Cheng Juan, Chen Xiang, Lu Zhiyuan, Zhang Xu, Zhao Zhangyan
Department of Electronics Science & Technology, Univ. of Science & Technology of China, Hefei 230027, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2011 Apr;28(2):352-6, 370.
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.
本文报道了基于表面肌电（SEMG）信号的手指按键手势模式识别研究。所有手势均参照PC标准键盘进行定义，共定义了16种与右手相关的按键手势。通过4个传感器从受试者前臂采集SEMG信号。设计并实施了两种模式识别实验，以探索基于SEMG信号的按键手势识别的可行性和可重复性。6名受试者的结果表明，使用当天模板时，16种定义的按键手势的平均分类率达到75.8%以上。此外，当训练样本累计达到5天时，识别准确率接近使用当天模板时获得的准确率。实验结果证实了基于SEMG的按键手势分类的可行性和可重复性，这对于基于肌电控制的虚拟键盘交互的实现具有重要意义。
