Rezaee Khosro, Khavari Safoura Farsi, Ansari Mojtaba, Zare Fatemeh, Roknabadi Mohammad Hossein Alizadeh
Department of Biomedical Engineering, Meybod University, Meybod, Iran.
Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
Sci Rep. 2024 Dec 28;14(1):31257. doi: 10.1038/s41598-024-82676-1.
Surface electromyography (sEMG) data has been extensively utilized in deep learning algorithms for hand movement classification. This paper aims to introduce a novel method for hand gesture classification using sEMG data, addressing accuracy challenges seen in previous studies. We propose a U-Net architecture incorporating a MobileNetV2 encoder, enhanced by a novel Bidirectional Long Short-Term Memory (BiLSTM) and metaheuristic optimization for spatial feature extraction in hand gesture and motion recognition. Bayesian optimization is employed as the metaheuristic approach to optimize the BiLSTM model's architecture. To address the non-stationarity of sEMG signals, we employ a windowing strategy for signal augmentation within deep learning architectures. The MobileNetV2 encoder and U-Net architecture extract relevant features from sEMG spectrogram images. Edge computing integration is leveraged to further enhance innovation by enabling real-time processing and decision-making closer to the data source. Six standard databases were utilized, achieving an average accuracy of 90.23% with our proposed model, showcasing a 3-4% average accuracy improvement and a 10% variance reduction. Notably, Mendeley Data, BioPatRec DB3, and BioPatRec DB1 surpassed advanced models in their respective domains with classification accuracies of 88.71%, 90.2%, and 88.6%, respectively. Experimental results underscore the significant enhancement in generalizability and gesture recognition robustness. This approach offers a fresh perspective on prosthetic management and human-machine interaction, emphasizing its efficacy in improving accuracy and reducing variance for enhanced prosthetic control and interaction with machines through edge computing integration.
表面肌电图（sEMG）数据已在用于手部运动分类的深度学习算法中得到广泛应用。本文旨在介绍一种使用sEMG数据进行手势分类的新方法，以应对先前研究中出现的准确性挑战。我们提出了一种结合MobileNetV2编码器的U-Net架构，并通过新颖的双向长短期记忆（BiLSTM）和元启发式优化进行增强，用于手势和运动识别中的空间特征提取。采用贝叶斯优化作为元启发式方法来优化BiLSTM模型的架构。为了解决sEMG信号的非平稳性问题，我们在深度学习架构中采用了一种加窗策略进行信号增强。MobileNetV2编码器和U-Net架构从sEMG频谱图图像中提取相关特征。利用边缘计算集成，通过在更靠近数据源的位置进行实时处理和决策，进一步增强创新性。使用了六个标准数据库，我们提出的模型平均准确率达到90.23%，平均准确率提高了3-4%，方差降低了10%。值得注意的是，Mendeley Data、BioPatRec DB3和BioPatRec DB1在各自领域超越了先进模型，分类准确率分别为88.71%、90.2%和88.6%。实验结果强调了在泛化性和手势识别鲁棒性方面的显著提升。这种方法为假肢管理和人机交互提供了新的视角，强调了其在提高准确性和降低方差方面的功效，以通过边缘计算集成实现更好的假肢控制和与机器的交互。
