轻量级EDA去噪模型，适配穿戴设备在剧烈运动和水下环境的健康监测。
Memory-Efficient EDA Denoising via Knowledge Distillation for Wearable IoT Under Severe Motion Artifacts and Underwater Conditions
- 用师生模型知识蒸馏，压缩模型至0.51MB，计算量降为11.61M FLOPs
- 水下测试中误差从2.809降至0.215，信噪比提升12.08dB
- 可提前6.9分钟预测神经症状，提升临床预警灵敏度
皮肤电活动（EDA）广泛用于可穿戴医疗物联网（IoMT）系统进行连续健康监测，包括自主神经评估。然而，EDA信号极易受运动伪影和环境噪声影响，限制了其在水下等恶劣条件下的可靠部署。本研究提出一种鲁棒、可部署的EDA去噪框架，能跨不同测量位置和严苛环境泛化。该框架采用混合CNN-Transformer教师模型与轻量级深度可分离卷积学生模型，通过知识蒸馏（KD）策略实现高效压缩。为进一步提升鲁棒性，引入真实数据增强方案以模拟多样运动伪影与环境失真。基于公开数据集验证，学生模型显著降低模型大小（7.87 MB → 0.51 MB）和计算成本（105.1M → 11.61M FLOPs），同时保持优异去噪性能（MAE: 0.144，SNR提升：12.08 dB）。在真实水下环境测试（UMAC数据集）中，该方法大幅改善皮肤电反应重建，均方误差由2.809降至0.215。在独立测试（CNS-OT数据集）中，去噪后信号提升下游预测性能，达到最高AUROC（0.806），早期预测灵敏度从0.550提升至0.767，实现症状发生前平均6.9分钟的预测能力。结果表明，该框架不仅提升信号质量，还增强临床相关预测性能，且适用于资源受限的可穿戴物联网系统在严苛环境中的部署。
原文摘要 · Abstract (English)
Electrodermal activity (EDA) is widely used in wearable Internet of Medical Things (IoMT) systems for continuous health monitoring, including autonomic assessment. However, EDA signals are highly vulnerable to motion artifacts and environmental noise, limiting reliable deployment in harsh operating conditions such as underwater. This study proposes a robust, deployable EDA denoising framework that generalizes across multiple measurement locations and harsh environments. The framework integrates a hybrid CNN-Transformer teacher model with a lightweight depth-wise separable CNN student model via a knowledge distillation (KD) strategy. To further improve robustness, a realistic data augmentation scheme is introduced to simulate diverse motion artifacts and environmental distortions. The KD-based student model significantly reduces model size (7.87 MB to 0.51 MB) and computational cost (105.1M to 11.61M FLOPs) while maintaining denoising performance (MAE: 0.144, SNR improvement: 12.08 dB) using the public dataset validation. In real-world underwater conditions (UMAC dataset) testing, the proposed method substantially improves skin conductance response reconstruction, reducing mean absolute error from 2.809 to 0.215. Furthermore, on independent testing using the CNS-OT dataset, the denoised signals enhanced downstream CNS-OT prediction performance, achieving the highest AUROC (0.806) compared to prior denoising methods. The proposed method also improved the early prediction rate (sensitivity) from 0.550 to 0.767, enabling CNS-OT prediction up to a median of 6.9 minutes before symptom onset. These results demonstrate that the proposed framework not only improves EDA signal quality but also enhances clinically relevant prediction performance while remaining suitable for deployment in resource-constrained wearable Internet of Things systems operating in harsh environments.
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