基于表面肌电图的独立于用户的实时手势识别

Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services Pub Date : 2017-09-04 DOI:10.1145/3098279.3098553

Frederic Kerber, M. Puhl, A. Krüger

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引用次数: 63

摘要

本文提出了一种基于表面肌电图的实时手势识别系统。我们采用了一种基于支持向量机的用户独立方法，该方法利用了从Thalmic Labs从Myo臂环获得的原始肌电图数据中提取的十个特征。通过改进的同步方法，简化了传感臂带的应用过程。我们报告了一项用户研究的结果，14名参与者使用由40个手势组成的扩展集。考虑到Myo臂环目前支持的五种现成手势，我们在相同数据集上以95%的总体准确率优于他们的方法，而原始算法的总体准确率为68%。

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User-independent real-time hand gesture recognition based on surface electromyography

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.

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Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services

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