Research on finger key-press gesture recognition based on surface electromyographic signals.
- Author:
Juan CHENG
1
;
Xiang CHEN
;
Zhiyuan LU
;
Xu ZHANG
;
Zhangyan ZHAO
Author Information
1. Department of Electronics Science & Technology, Univ. of Science & Technology of China, Hefei 230027, China.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Electromyography;
methods;
Fingers;
Gestures;
Humans;
Movement;
physiology;
Muscle, Skeletal;
physiology;
Pattern Recognition, Automated;
methods;
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2011;28(2):352-370
- CountryChina
- Language:Chinese
-
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.
