Ben Haj Amor Amina, El Ghoul Oussama, Jemni Mohamed
Research Laboratory LaTICE, University of Tunis, Tunisia.
Data Brief. 2023 Nov 4;51:109770. doi: 10.1016/j.dib.2023.109770. eCollection 2023 Dec.
Nowadays, surface electromyography (sEMG) is evolving as a technology for hand gesture recognition. Detailed studies have revealed the capacity of EMG signals to access detailed information, particularly in the classification of hand gestures. Indeed, this advancement emerges as an interesting element in refining the recognition and interpretation of sign languages and exploring deeper into the phonology of signed languages. Aligned with this advancement and the need for a reliable and mobile sign language recognition system, we introduce a specialized sEMG dataset, acquired using the Myo armband. This device is adept at capturing recordings at frequencies of up to 200 Hz. The dataset focuses on the 28 letters of the Arabic alphabet and 10 digits using hand gestures, with each gesture captured into 400 frames. This considerable collection of 18,716 samples was achieved with the cooperation of three contributors, providing a varied and comprehensive range of gestural data.
如今，表面肌电图（sEMG）正发展成为一种用于手势识别的技术。详细研究揭示了肌电信号获取详细信息的能力，特别是在手势分类方面。事实上，这一进展成为完善手语识别和解释以及深入探索手语音系学的一个有趣元素。与这一进展以及对可靠且可移动的手语识别系统的需求相一致，我们引入了一个使用Myo臂带采集的专门的sEMG数据集。该设备擅长以高达200Hz的频率进行记录。该数据集聚焦于使用手势表示的阿拉伯字母表中的28个字母和10个数字，每个手势被捕捉成400帧。在三位贡献者的合作下，获得了这一包含18716个样本的可观集合，提供了多样且全面的手势数据。
