在噪声干扰下，物理特征工程比复杂模型更准地无创测血糖
Physics-Informed Neural Networks vs. Physics Models for Non-Invasive Glucose Monitoring: A Comparative Study Under Noise-Stressed Synthetic Conditions
- 用物理规律设计特征，而非依赖深度网络
- 最低误差13.6 mg/dL，仅需56参数、0.01毫秒推理
- 适合低信噪比场景，如真实世界穿戴设备
非侵入式血糖监测在非受控环境下受低信噪比（SNR）主导：硬件漂移、环境变化和生理因素抑制了近红外（NIR）信号中的葡萄糖特征。我们构建了一个噪声强化的NIR模拟器，注入12位模数转换量化、LED漂移、光电二极管暗噪声、温湿度变化、接触压力噪声、菲茨帕特里克肤色Ⅰ–Ⅵ型色素及葡萄糖波动，形成低相关性环境（葡萄糖与NIR相关系数rho=0.21）。在此平台上，我们对比六种方法：增强型比尔-朗伯（物理工程岭回归）、原始PINN、优化PINN、RTE启发式PINN、选择性RTE PINN及浅层DNN。物理工程的比尔-朗伯模型以仅56个参数、0.01毫秒推理时间，实现13.6 mg/dL RMSE，优于更深的PINNs和浅层DNN基线，在低信噪比条件下表现最佳。研究将任务重新定义为弱信号下的噪声抑制，表明精心设计的物理特征可超越高容量模型。
原文摘要 · Abstract (English)
Non-invasive glucose monitoring outside controlled settings is dominated by low signal-to-noise ratio (SNR): hardware drift, environmental variation, and physiology suppress the glucose signature in NIR signals. We present a noise-stressed NIR simulator that injects 12-bit ADC quantisation, LED drift, photodiode dark noise, temperature/humidity variation, contact-pressure noise, Fitzpatrick I-VI melanin, and glucose variability to create a low-correlation regime (rho_glucose-NIR = 0.21). Using this platform, we benchmark six methods: Enhanced Beer-Lambert (physics-engineered ridge regression), Original PINN, Optimised PINN, RTE-inspired PINN, Selective RTE PINN, and a shallow DNN. The physics-engineered Beer Lambert model achieves the lowest error (13.6 mg/dL RMSE) with only 56 parameters and 0.01 ms inference, outperforming deeper PINNs and the SDNN baseline under low-SNR conditions. The study reframes the task as noise suppression under weak signal and shows that carefully engineered physics features can outperform higher-capacity models in this regime.
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。