提出可解释的信号阵列域适应层，解决穿戴设备电极位移导致的性能下降问题。
Spatial Adaptation Layer: Interpretable Domain Adaptation For Biosignal Sensor Array Applications
- 设计空间自适应层，学习输入间的仿射变换以补偿电极偏移
- 在两个高密度肌电数据集上超越微调，参数量少且性能稳定
- 可解释性强，证明前臂周向移动是主要影响因素，适合医疗穿戴场景
机器学习在可穿戴设备信号处理中前景广阔，如表面肌电（sEMG）和脑电图（EEG）。然而，尽管会话内表现良好，会话间性能常受电极位移影响。现有方法多依赖大规模昂贵数据集，或缺乏鲁棒性与可解释性。为此，我们提出空间自适应层（SAL），可嵌入任意生物信号阵列模型，学习两记录会话间的参数化仿射变换。同时引入可学习基线归一化（LBN）以减少基线波动。在两个高密度sEMG手势识别数据集上验证，SAL与LBN优于标准微调，在普通阵列上表现优异，即使使用逻辑回归也具竞争力，参数量仅为原模型的数个数量级，且具有物理可解释性。消融实验表明，前臂周向平移是性能提升的主要原因。
原文摘要 · Abstract (English)
Machine learning offers promising methods for processing signals recorded with wearable devices such as surface electromyography (sEMG) and electroencephalography (EEG). However, in these applications, despite high within-session performance, intersession performance is hindered by electrode shift, a known issue across modalities. Existing solutions often require large and expensive datasets and/or lack robustness and interpretability. Thus, we propose the Spatial Adaptation Layer (SAL), which can be applied to any biosignal array model and learns a parametrized affine transformation at the input between two recording sessions. We also introduce learnable baseline normalization (LBN) to reduce baseline fluctuations. Tested on two HD-sEMG gesture recognition datasets, SAL and LBN outperformed standard fine-tuning on regular arrays, achieving competitive performance even with a logistic regressor, with orders of magnitude less, physically interpretable parameters. Our ablation study showed that forearm circumferential translations account for the majority of performance improvements.
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