AN EMG DATASET FOR ARABIC SIGN LANGUAGE ALPHABET LETTERS AND NUMBERS

ABSTRACT Sign languages are natural, gestural languages that use visual channel to communicate. Deaf people develop them to overcome their inability to communicate orally. Sign language interpreters bridge the gap that deaf people face in society and provide them with an equal opportunity to thrive in all environments. However, Deaf people often struggle to communicate on a daily basis, especially in public service spaces such as hospitals, post offices, and municipal buildings. Therefore, the implementation of a tool for automatic recognition of sign language is essential to allow the autonomy of deaf people. Moreover, it is difficult to provide full-time interpreters to help deaf people in all public services and administrations. Although surface electromyography (sEMG) provides an important potential technology for the detection of hand gestures, the related research in automatic SL recognition remains limited. To date, most works have focused on the recognition of hand gestures from images, videos, or gloves. The works of BEN HAJ AMOR et al. on EMG signals have shown that these multichannel signals contain rich and detailed information that can be exploited, in particular for the recognition of handshape and for the control prosthesis. Consequently, these successes represent a great step towards the recognition of gestures in sign language. We build a large database of EMG data, recorded while signing the 28 characters of the Arabic sign language alphabet. This provides a valuable resource for research into how the muscles involved in signing produce the shapes needed to form the letters of the alphabet. Instructions: The data for this project is provided as zipped NumPy arrays with custom headers. In order to load these files, you will need to have the NumPy package installed. The respective loadz primitive allows for a straight forwardloading of the datasets. The data is organized as follows: The data for each label (handshape) is stored in a separate folder. Each folder contains .npz files. An npz file contains the data for one record (a matrix 8x400). For more details, please refer to the paper.## 摘要 手语（Sign Languages）是依托视觉通道实现交流的自然手势语言。听障人群为克服口头交流障碍而发展出手语。手语翻译能够弥合听障群体在社会中面临的交流鸿沟，帮助他们在各类场景中获得平等发展的机会。然而，听障人群在日常交流中仍常遇阻碍，尤其在医院、邮局、市政大楼等公共服务场所。因此，开发手语自动识别工具对保障听障群体的自主交流至关重要。此外，为所有公共服务与行政场景配备专职手语翻译难度极大。 尽管表面肌电（surface electromyography, sEMG）为手部手势检测提供了极具潜力的技术路径，但当前手语自动识别领域的相关研究仍较为有限。迄今为止，绝大多数相关研究聚焦于基于图像、视频或数据手套的手势识别。BEN HAJ AMOR等人针对肌电信号的研究表明，多通道肌电信号蕴含丰富且细致的可利用信息，尤其可用于手部手型识别与假肢控制。上述研究成果为手语手势识别领域迈出了重要一步。 本研究构建了一套大规模肌电数据集，采集自阿拉伯手语字母表28个字符的手语动作过程。该数据集为探究手语动作中相关肌肉如何生成字母所需手型的研究提供了宝贵资源。 ## 使用说明 本项目数据集以带自定义文件头的压缩NumPy数组（NumPy arrays）格式提供。若要加载此类文件，需预先安装NumPy工具库。 专用loadz原语可直接完成数据集加载。数据集的组织形式如下： 每个标签（对应手部手型）的数据均存储于独立文件夹中，每个文件夹内包含若干.npz格式文件。单个.npz文件对应一条记录的数据（为8×400的矩阵）。 如需了解更多细节，请参阅相关论文。