Background and objectives

Surface Electromyography (sEMG) is used mostly for neuromuscular diagnosis, assistive technology, physical rehabilitation, and human-computer interactions. Achieving a precise and lightweight method along with low latency for gesture recognition is still a real-life challenge, especially for rehabilitation and assistive robots. This work aims to introduce a highly accurate and lightweight deep learning method for gesture recognition.

Methods

High-density sEMG, unlike sparse sEMG, does not require accurate electrode placement and provides more physiological information. Then we apply high-density sEMG, which, according to previous studies, leads to sEMG images. In this study, we introduce the Sensor-Wise method, which has a higher capability to extract features compared to the sEMG image method due to its high compatibility with the nature of sEMG signals and the structure of convolutional networks.

Results

The proposed method, because of its optimal structure with only two hidden layers and its high compatibility, has shown no sign of overfitting and was able to reach an accuracy of almost 100% (99.99%) when it was evaluated by CapgMyo DB-a database through 96 electrodes. Using this method, even with 16 electrodes, we were able to reach an accuracy of 99.8%, which was higher than the accuracies reported in the previous studies. Additionally, the method was evaluated by the CSL-HDEMG database, where the accuracy reached 99.55%. Previous studies either introduced expensive computational methods with overfitting or reported lower accuracies compared to this study.

Conclusions

The Sensor- Wise method has high compatibility with the nature of sEMG signals and the structure of convolutional networks. The high accuracy and lightweight structure of this method with only two hidden layers make it a proper option for hardware implementation.

中文翻译：

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基于表面肌电的高精度轻量级深度学习手势识别方法。

背景和目标

表面肌电图（sEMG）主要用于神经肌肉诊断，辅助技术，身体康复和人机交互。实现精确，轻巧的方法以及低延迟的手势识别仍然是现实生活中的挑战，尤其是对于康复和辅助机器人而言。这项工作旨在介绍一种用于手势识别的高度准确，轻量级的深度学习方法。

方法

与稀疏sEMG不同，高密度sEMG不需要精确的电极放置，并且可以提供更多的生理信息。然后，我们应用高密度sEMG，根据先前的研究，这会生成sEMG图像。在这项研究中，我们介绍了Sensor-Wise方法，由于它与sEMG信号的性质和卷积网络的结构高度兼容，因此与sEMG图像方法相比，它具有更高的特征提取能力。

结果

所提出的方法由于具有只有两个隐藏层的最佳结构，并且具有很高的兼容性，因此没有出现过拟合的迹象，并且在CapgMyo DB-a数据库进行评估时，能够达到几乎100％（99.99％）的准确度通过96个电极。使用这种方法，即使使用16个电极，我们也可以达到99.8％的准确度，这比以前的研究报告的准确性更高。此外，该方法还通过CSL-HDEMG数据库进行了评估，该数据库的准确性达到了99.55％。以前的研究要么引入了过拟合的昂贵计算方法，要么报告的准确性低于本研究。

结论

Sensor-Wise方法与sEMG信号的性质和卷积网络的结构具有高度的兼容性。这种方法只有两个隐藏层的高精确度和结构轻巧，使其硬件实现一个合适的选择。