Yang Jaemo, Cha Doheun, Lee Dong-Gyu, Ahn Sangtae
School of Electronics Engineering, Kyungpook National University, Daegu, South Korea.
School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea.
Comput Biol Med. 2025 Feb;185:109525. doi: 10.1016/j.compbiomed.2024.109525. Epub 2024 Dec 13.
This paper introduces the Spatio-Temporal Cross Network (STCNet), a novel deep learning architecture tailored for robust hand gesture recognition in surface electromyography (sEMG) across multiple subjects. We address the challenges associated with the inter-subject variability and environmental factors such as electrode shift and muscle fatigue, which traditionally undermine the robustness of gesture recognition systems. STCNet integrates a convolutional-recurrent architecture with a spatio-temporal block that extracts features over segmented time intervals, enhancing both spatial and temporal analysis. Additionally, a rolling convolution technique designed to reflect the circular band structure of the sEMG measurement device is incorporated, thus capturing the inherent spatial relationships more effectively. We further propose a subject-aware contrastive learning framework that utilizes both subject and gesture label information to align the representation of vector space. Our comprehensive experimental evaluations demonstrate the superiority of STCNet under aggregated conditions, achieving state-of-the-art performance on benchmark datasets and effectively managing the variability among different subjects. The implemented code can be found at https://github.com/KNU-BrainAI/STCNet.
本文介绍了时空交叉网络（STCNet），这是一种新颖的深度学习架构，专为跨多个受试者的表面肌电图（sEMG）中的鲁棒手势识别而设计。我们解决了与受试者间变异性以及诸如电极移位和肌肉疲劳等环境因素相关的挑战，这些传统上会削弱手势识别系统的鲁棒性。STCNet将卷积循环架构与一个时空块集成在一起，该时空块在分段时间间隔上提取特征，增强了空间和时间分析。此外，还引入了一种旨在反映sEMG测量设备的圆形带结构的滚动卷积技术，从而更有效地捕捉固有的空间关系。我们进一步提出了一个受试者感知对比学习框架，该框架利用受试者和手势标签信息来对齐向量空间的表示。我们全面的实验评估证明了STCNet在聚合条件下的优越性，在基准数据集上实现了领先的性能，并有效地管理了不同受试者之间的变异性。实现的代码可在https://github.com/KNU-BrainAI/STCNet上找到。
