Title: Muscle Anatomy-aware Geometric Deep Learning for sEMG-based Gesture Decoding Show Chinese title

Title: 肌肉解剖感知的几何深度学习用于sEMG手势解码

Abstract: Robust and accurate decoding of gesture from non-invasive surface electromyography (sEMG) is important for various applications including spatial computing, healthcare, and entertainment, and has been actively pursued by researchers and industry. Majority of sEMG-based gesture decoding algorithms employ deep neural networks that are designed for Euclidean data, and may not be suitable for analyzing multi-dimensional, non-stationary time-series with long-range dependencies such as sEMG. State-of-the-art sEMG-based decoding methods also demonstrate high variability across subjects and sessions, requiring re-calibration and adaptive fine-tuning to boost performance. To address these shortcomings, this work proposes a geometric deep learning model that learns on symmetric positive definite (SPD) manifolds and leverages unsupervised domain adaptation to desensitize the model to subjects and sessions. The model captures the features in time and across sensors with multiple kernels, projects the features onto SPD manifold, learns on manifolds and projects back to Euclidean space for classification. It uses a domain-specific batch normalization layer to address variability between sessions, alleviating the need for re-calibration or fine-tuning. Experiments with publicly available benchmark gesture decoding datasets (Ninapro DB6, Flexwear-HD) demonstrate the superior generalizability of the model compared to Euclidean and other SPD-based models in the inter-session scenario, with up to 8.83 and 4.63 points improvement in accuracy, respectively. Detailed analyses reveal that the model extracts muscle-specific information for different tasks and ablation studies highlight the importance of modules introduced in the work. The proposed method pushes the state-of-the-art in sEMG-based gesture recognition and opens new research avenues for manifold-based learning for muscle signals. Show Chinese abstract

Abstract: 从非侵入性表面肌电图（sEMG）中稳健且准确的姿势解码对于空间计算、医疗保健和娱乐等各种应用非常重要，并且一直受到研究人员和工业界的积极追求。 大多数基于sEMG的姿势解码算法采用为欧几里得数据设计的深度神经网络，可能不适合分析具有长程依赖性的多维非平稳时间序列，如sEMG。 最先进的基于sEMG的解码方法在不同受试者和会话中也表现出高变异性，需要重新校准和自适应微调以提高性能。 为了解决这些不足，本工作提出了一种几何深度学习模型，在对称正定（SPD）流形上进行学习，并利用无监督领域自适应来减少模型对受试者和会话的敏感性。 该模型使用多个内核捕捉时间和传感器上的特征，将特征投影到SPD流形上，在流形上进行学习，然后将特征投影回欧几里得空间进行分类。 它使用特定领域的批量归一化层来解决会话之间的变异性，减轻了重新校准或微调的需要。 在公开可用的基准姿势解码数据集（Ninapro DB6, Flexwear-HD）上的实验表明，与欧几里得和其他基于SPD的模型相比，该模型在跨会话场景中表现出更优越的泛化能力，分别提高了8.83和4.63个百分点的准确性。 详细的分析表明，该模型为不同的任务提取肌肉特异性信息，消融研究强调了本工作中引入模块的重要性。 所提出的方法推动了基于sEMG的姿势识别的最先进水平，并为基于流形的学习在肌肉信号中的研究开辟了新的研究方向。