Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece.
Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium.
Sensors (Basel). 2020 Aug 29;20(17):4892. doi: 10.3390/s20174892.
The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most common approach to deal with this data shortage in other research domains. In the case of surface electromyography (sEMG) signals, there is limited research in augmentation methods and quite regularly the results differ between available studies. In this work, we provide a detailed evaluation of existing (i.e., additive noise, overlapping windows) and novel (i.e., magnitude warping, wavelet decomposition, synthetic sEMG models) strategies of data augmentation for electromyography signals. A set of metrics (i.e., classification accuracy, silhouette score, and Davies-Bouldin index) and visualizations help with the assessment and provides insights about their performance. Methods like signal magnitude warping and wavelet decomposition yield considerable increase (up to 16%) in classification accuracy across two benchmark datasets. Particularly, a significant improvement of 1% in the classification accuracy of the state-of-the-art model in hand gesture recognition is achieved.
基于肌电图的手势识别的应用范围近年来有所增加。在文献中经常遇到的一个共同问题是数据可用性不足。数据扩充旨在从现有数据中生成新的合成数据，这是解决其他研究领域数据短缺的最常见方法。在表面肌电图 (sEMG) 信号的情况下，扩充方法的研究有限，并且在可用研究之间结果经常不同。在这项工作中，我们详细评估了现有的（即加性噪声、重叠窗口）和新颖的（即幅度扭曲、小波分解、合成 sEMG 模型）肌电图信号扩充策略。一组度量标准（即分类准确性、轮廓得分和 Davies-Bouldin 指数）和可视化有助于评估，并提供有关其性能的见解。信号幅度扭曲和小波分解等方法可将两个基准数据集的分类准确性提高相当大的幅度（高达 16%）。特别地，在手势识别方面，实现了最先进模型的分类准确性提高 1%。
