title: [Research on finger key-press gesture recognition based on surface electromyographic signals].
abstract: 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.
authors: Department of Electronics Science & Technology, Univ. of Science & Technology of China, Hefei 230027, China.
authors: Juan
authors: J
authors: Cheng
authors: Xiang
authors: X
authors: Chen
authors: Zhiyuan
authors: Z
authors: Lu
authors: Xu
authors: X
authors: Zhang
authors: Zhangyan
authors: Z
authors: Zhao