IEEE Trans Neural Syst Rehabil Eng. 2022;30:1252-1260. doi: 10.1109/TNSRE.2022.3173708. Epub 2022 May 17.
In order to reduce the gap between the laboratory environment and actual use in daily life of human-machine interaction based on surface electromyogram (sEMG) intent recognition, this paper presents a benchmark dataset of sEMG in non-ideal conditions (SeNic). The dataset mainly consists of 8-channel sEMG signals, and electrode shifts from an 3D-printed annular ruler. A total of 36 subjects participate in our data acquisition experiments of 7 gestures in non-ideal conditions, where non-ideal factors of 1) electrode shifts, 2) individual difference, 3) muscle fatigue, 4) inter-day difference, and 5) arm postures are elaborately involved. Signals of sEMG are validated first in temporal and frequency domains. Results of recognizing gestures in ideal conditions indicate the high quality of the dataset. Adverse impacts in non-ideal conditions are further revealed in the amplitudes of these data and recognition accuracies. To be concluded, SeNic is a benchmark dataset that introduces several non-ideal factors which often degrade the robustness of sEMG-based systems. It could be used as a freely available dataset and a common platform for researchers in the sEMG-based recognition community. The benchmark dataset SeNic are available online via the website (https://github.com/bozhubo/SeNic and https://gitee.com/bozhubo/SeNic).
为了缩小基于表面肌电信号（sEMG）意图识别的人机交互在实验室环境与日常生活实际应用之间的差距，本文提出了一个 sEMG 在非理想条件下的基准数据集（SeNic）。该数据集主要由 8 通道 sEMG 信号和从 3D 打印的环形标尺上发生电极移位组成。共有 36 名受试者参与了我们在非理想条件下进行的 7 个手势的数据采集实验，其中详细涉及了非理想因素 1）电极移位、2）个体差异、3）肌肉疲劳、4）日内差异和 5）手臂姿势。sEMG 的信号首先在时域和频域进行验证。在理想条件下识别手势的结果表明了该数据集的高质量。在这些数据的幅度和识别准确率方面进一步揭示了非理想条件下的不利影响。总之，SeNic 是一个基准数据集，引入了几个经常降低基于 sEMG 的系统鲁棒性的非理想因素。它可以作为一个免费可用的数据集和基于 sEMG 识别社区的研究人员的共同平台。基准数据集 SeNic 可通过以下网址在线获取（https://github.com/bozhubo/SeNic 和 https://gitee.com/bozhubo/SeNic）。
