用Mamba架构提升肌电手势识别准确率，解决跨会话差异问题。
MoEMba: A Mamba-based Mixture of Experts for High-Density EMG-based Hand Gesture Recognition
- 基于选择性状态空间模型捕捉肌电信号时序与通道交互关系。
- 在CapgMyo数据集上达到56.9%的平衡准确率，优于现有方法。
- 适合需要高鲁棒性肌电手势识别的可穿戴设备研发者。
高密度表面肌电（HD-sEMG）已成为人机交互（HCI）的关键资源，能直接反映肌肉活动与运动意图。然而，实际应用中存在跨会话与跨被试分类准确率低的问题，会话间差异可达40%，源于HD-sEMG信号的固有时序变异性。针对此挑战，本文提出MoEMba框架，一种基于选择性状态空间模型（SSMs）的新方法，用于增强HD-sEMG手势识别。该框架通过通道注意力机制捕捉时序依赖与跨通道交互，并引入小波特征调制以提取多尺度时空关系，提升信号表征能力。在CapgMyo HD-sEMG数据集上的实验表明，MoEMba实现了56.9%的平衡准确率，优于当前最优方法。其对会话间变异性的强鲁棒性及对高维多变量时间序列数据的高效处理能力，凸显了其在推进HD-sEMG驱动的人机交互系统方面的潜力。
原文摘要 · Abstract (English)
High-Density surface Electromyography (HDsEMG) has emerged as a pivotal resource for Human-Computer Interaction (HCI), offering direct insights into muscle activities and motion intentions. However, a significant challenge in practical implementations of HD-sEMG-based models is the low accuracy of inter-session and inter-subject classification. Variability between sessions can reach up to 40% due to the inherent temporal variability of HD-sEMG signals. Targeting this challenge, the paper introduces the MoEMba framework, a novel approach leveraging Selective StateSpace Models (SSMs) to enhance HD-sEMG-based gesture recognition. The MoEMba framework captures temporal dependencies and cross-channel interactions through channel attention techniques. Furthermore, wavelet feature modulation is integrated to capture multi-scale temporal and spatial relations, improving signal representation. Experimental results on the CapgMyo HD-sEMG dataset demonstrate that MoEMba achieves a balanced accuracy of 56.9%, outperforming its state-of-the-art counterparts. The proposed framework's robustness to session-to-session variability and its efficient handling of high-dimensional multivariate time series data highlight its potential for advancing HD-sEMG-powered HCI systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。