School of Biomedical Engineering, Anhui Medical University, Hefei 230032, China.
Sensors (Basel). 2021 Oct 22;21(21):7002. doi: 10.3390/s21217002.
Surface electromyography (sEMG) is a kind of biological signal that records muscle activity noninvasively, which is of great significance in advanced human-computer interaction, prosthetic control, clinical therapy, and biomechanics. However, the number of hand gestures that can be recognized is limited and the recognition accuracy needs to be further improved. These factors lead to the fact that sEMG products are not widely used in practice. The main contributions of this paper are as follows. Firstly, considering the increasing number of gestures to be recognized and the complexity of gestures, an extensible two-stage machine learning lightweight framework was innovatively proposed for multi-gesture task recognition. Secondly, the multivariate variational mode decomposition (MVMD) is applied to extract the spatial-temporal features from the multiple channels to the EMG signals, and the separable convolutional neural network is used for modelling. In this work, the experimental results for 52 hand gestures recognition task show that the average accuracy on each stage is about 90%. The potential movement information is mainly contained in the low-frequency oscillator of the sEMG signal, and the model performs better with the low-frequency oscillation from the MVMD algorithm on the second stage classification than that of other decomposition methods.
表面肌电图（sEMG）是一种记录肌肉活动的非侵入式生物信号，在高级人机交互、假肢控制、临床治疗和生物力学等领域具有重要意义。然而，可识别的手势数量有限，识别精度有待进一步提高。这些因素导致 sEMG 产品在实际应用中并未得到广泛应用。本文的主要贡献如下。首先，考虑到要识别的手势数量不断增加和手势的复杂性，创新性地提出了一种可扩展的两阶段机器学习轻量级框架，用于多手势任务识别。其次，应用多元变分模态分解（MVMD）从多个通道的 EMG 信号中提取时空特征，并使用可分离卷积神经网络进行建模。在这项工作中，针对 52 个手势识别任务的实验结果表明，每个阶段的平均准确率约为 90%。sEMG 信号的潜在运动信息主要包含在低频振荡器中，与其他分解方法相比，该模型在第二阶段分类中使用 MVMD 算法的低频振荡时表现更好。
