User-Independent Real-Time Hand Gesture Recognition Based on Surface Electromyography

被引：24

作者：

Kerber, Frederic ^{[1
]}

Puhl, Michael ^{[2
]}

Krueger, Antonio ^{[1
]}

机构：

[1] German Res Ctr Artificial Intelligence DFKI, Saarland Informat Campus, Saarbrucken, Germany

[2] Saarland Univ, Saarland Informat Campus, Saarbrucken, Germany

来源：

PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON HUMAN-COMPUTER INTERACTION WITH MOBILE DEVICES AND SERVICES (MOBILEHCI '17) | 2017年

关键词：

Electromyography; gestural input; hand gestures;

D O I：

10.1145/3098279.3098553

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we present a novel real-time hand gesture recognition system based on surface electromyography. We employ a user-independent approach based on a support vector machine utilizing ten features extracted from the raw electromyographic data obtained from the Myo armband by Thalmic Labs. Through an improved synchronization approach, we simplified the application process of the sensing armband. We report the results of a user study with 14 participants using an extended set consisting of 40 gestures. Considering the set of five hand gestures currently supported off-the-shelf by the Myo armband, we outperform their approach with an overall accuracy of 95% compared to 68% with the original algorithm on the same dataset.

引用

页数：7

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