A testing system for a real-time gesture classification using surface EMG

被引：27
|
作者：
Akhmadeev, Konstantin
Rampone, Elena
Yu, Tianyi
Aoustin, Yannick
Le Carpentier, Eric
机构：
来源：
IFAC PAPERSONLINE | 2017年 / 50卷 / 01期
关键词：
Electromyography; EMG; myoelectric control; classification; support vector machine;
D O I：
10.1016/j.ifacol.2017.08.1602
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This study addresses the development of a testing system for pattern-recognition based strategies of myoelectric control. This text describes the structure and components of the proposed system, as well as a process of its testing. The latter included an acquisition of an accompanying EMG, using Myo (TM) armband by Thalmic Labs Inc.(TM), for six different gestures (classes) from seven subjects, as well as its processing, feature extraction, training the classifier and further real-time validation. The results show that system provides acceptable classification rates. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：11498 / 11503
页数：6
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