Abstract
Electromyography (EMG) based hand gesture recognition converts forearm muscle activity into control commands for prosthetics, rehabilitation, and human computer interaction. This paper proposes a novel approach to EMG-based hand gesture recognition that uses fine-grained classification and presents XMANet, which unifies low-level local and high level semantic cues through cross layer mutual attention among shallow to deep CNN experts. Using stacked spectrograms and scalograms derived from the Short Time Fourier Transform (STFT) and Wavelet Transform (WT), we benchmark XMANet against ResNet50, DenseNet-121, MobileNetV3, and EfficientNetB0. Experimental results on the Grabmyo dataset indicate that, using STFT, the proposed XMANet model outperforms the baseline ResNet50, EfficientNetB0, MobileNetV3, and DenseNet121 models with improvement of approximately 1.72%, 4.38%, 5.10%, and 2.53%, respectively. When employing the WT approach, improvements of around 1.57%, 1.88%, 1.46%, and 2.05% are observed over the same baselines. Similarly, on the FORS EMG dataset, the XMANet(ResNet50) model using STFT shows an improvement of about 5.04% over the baseline ResNet50. In comparison, the XMANet(DenseNet121) and XMANet(MobileNetV3) models yield enhancements of approximately 4.11% and 2.81%, respectively. Moreover, when using WT, the proposed XMANet achieves gains of around 4.26%, 9.36%, 5.72%, and 6.09% over the baseline ResNet50, DenseNet121, MobileNetV3, and EfficientNetB0 models, respectively. These results confirm that XMANet consistently improves performance across various architectures and signal processing techniques, demonstrating the strong potential of fine grained features for accurate and robust EMG classification.
Abstract (translated)
肌电图（EMG）基于的手势识别技术将前臂肌肉活动转化为假肢控制、康复和人机交互的指令。本文提出了一种新的基于EMG的手势识别方法，该方法采用细粒度分类，并提出了XMANet模型，通过浅层到深层卷积神经网络专家之间的跨层相互注意力机制统一低级局部特征与高级语义线索。 使用从短时傅里叶变换（STFT）和小波变换（WT）导出的堆叠频谱图和斯卡洛格拉姆进行基准测试，我们将XMANet与ResNet50、DenseNet-121、MobileNetV3和EfficientNetB0进行了对比。在Grabmyo数据集上的实验结果表明，在使用STFT时，提出的XMANet模型比基线模型ResNet50、EfficientNetB0、MobileNetV3和DenseNet121分别提高了约1.72%、4.38%、5.10%和2.53%。同样，在使用WT方法时，与这些基准相比，XMANet的改进约为1.57%、1.88%、1.46%和2.05%。 在FORS EMG数据集上，当使用STFT时，基于ResNet50的XMANet模型比基线ResNet50提高了约5.04%。相比之下，基于DenseNet121和MobileNetV3的XMANet模型分别提供了大约4.11%和2.81%的改进。当使用WT时，提出的XMANet在基准ResNet50、DenseNet121、MobileNetV3和EfficientNetB0上分别获得了约4.26%、9.36%、5.72%和6.09%的增益。 这些结果确认了XMANet能够在多种架构和信号处理技术中持续提高性能，展示了细粒度特征在准确且鲁棒的EMG分类中的强大潜力。
URL
https://arxiv.org/abs/2504.14708
