Evaluating Sign Language Recognition Using the Myo Armband

João Gabriel Abreu, J. M. Teixeira, L. Figueiredo, V. Teichrieb
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引用次数: 79

Abstract

The successful recognition of sign language gestures by computer systems would greatly improve communications between the deaf and the hearers. This work evaluates the usage of electromyogram (EMG) data provided by the Myo armband as features for classification of 20 stationary letter gestures from the Brazilian Sign Language (LIBRAS) alphabet. The classification was performed by binary Support Vector Machines (SVMs), trained with a one-vs-all strategy. The results obtained show that it is possible to identify the gestures, but substantial limitations were found that would need to be tackled by further studies.
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使用Myo臂章评估手语识别
计算机系统对手语手势的成功识别将极大地改善聋哑人与听者之间的交流。这项工作评估了Myo臂章提供的肌电图(EMG)数据的使用,作为巴西手语(LIBRAS)字母表中20个固定字母手势的分类特征。分类由二元支持向量机(svm)执行,采用一比全策略训练。研究结果表明,识别这些手势是可能的,但我们发现了大量的局限性,需要进一步的研究来解决。
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