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Abstract：
Sign Language recognition systems aid communication among deaf people, hearing impaired people, and speakers. One of the types of signals that has seen increased studies and that can be used as input for these systems is surface electromyography (sEMG). This work presents the recognition of a set of alphabet gestures from Brazilian Sign Language (Libras) using sEMG acquired from an armband. Only sEMG signals were used as input. Signals from 12 subjects were acquired using a MyoTM armband for the 26 signs of the Libras alphabet. Additionally, as the sEMG has several signal processing parameters, the influence of segmentation, feature extraction, and classification was considered at each step of the pattern recognition. In segmentation, window length and the presence of four levels of overlap rates were analyzed, as well as the contribution of each feature, the literature feature sets, and new feature sets proposed for different classifiers. We found that the overlap rate had a high influence on this task. Accuracies in the order of 99% were achieved for the following factors: segments of 1.75 s with a 12.5% overlap rate; the proposed set of four features; and random forest (RF) classifiers.
摘要：
手语识别系统有助于聋人之间的交流，听力受损的人，和扬声器。表面肌电图（sEMG）是已经得到越来越多研究并且可以用作这些系统输入的信号类型之一。这项工作介绍了使用从臂章获得的sEMG识别巴西手语（Libras）的一组字母手势。仅sEMG信号用作输入。使用MyoTM臂章获取来自12名受试者的信号，以获取Libras字母的26个符号。此外,由于sEMG有几个信号处理参数，分割的影响，特征提取,在模式识别的每个步骤都考虑了分类。在分割中，窗口长度和存在四个水平的重叠率进行了分析，以及每个功能的贡献，文学特征集，以及针对不同分类器提出的新特征集。我们发现重叠率对这项任务有很大影响。对于以下因素，精度达到了99％左右：1.75s的片段，重叠率为12.5％；建议的四个特征集；和随机森林（RF）分类器。
