用低采样率PPG实现可穿戴设备快速精准血氧监测，无需临床校准。
Rapid Adaptation of SpO2 Estimation to Wearable Devices via Transfer Learning on Low-Sampling-Rate PPG
- 基于迁移学习，先在临床数据预训练，再用可穿戴设备数据微调。
- 25Hz采样下MAE仅2.624%，功耗降低40%。
- 适合资源受限的可穿戴健康设备，实现实时血氧波动捕捉。
血氧饱和度（SpO2）是医疗监测的关键指标。传统方法依赖复杂的临床校准，难以适用于低功耗可穿戴设备。本文提出一种基于迁移学习的框架，利用低采样率（25Hz）双通道光电容积脉搏波（PPG）实现对节能型可穿戴设备的快速准确血氧估计。首先在公开临床数据集上预训练带自注意力机制的双向LSTM模型，再使用我们自研的We-Be手环与经美国食品药品监督管理局（FDA）批准的参考脉搏血氧仪采集的数据进行微调。实验结果表明，该方法在公开数据集上平均绝对误差（MAE）为2.967%，在私有数据集上为2.624%，显著优于传统校准和未迁移的机器学习基线。此外，采用25Hz PPG使功耗相比100Hz降低40%（不含基线功耗）。该方法在瞬时血氧预测中也达到3.284%的MAE，有效捕捉快速波动。结果证明，无需临床校准即可实现高精度、低功耗的可穿戴血氧监测。
原文摘要 · Abstract (English)
Blood oxygen saturation (SpO2) is a vital marker for healthcare monitoring. Traditional SpO2 estimation methods often rely on complex clinical calibration, making them unsuitable for low-power, wearable applications. In this paper, we propose a transfer learning-based framework for the rapid adaptation of SpO2 estimation to energy-efficient wearable devices using low-sampling-rate (25Hz) dual-channel photoplethysmography (PPG). We first pretrain a bidirectional Long Short-Term Memory (BiLSTM) model with self-attention on a public clinical dataset, then fine-tune it using data collected from our wearable We-Be band and an FDA-approved reference pulse oximeter. Experimental results show that our approach achieves a mean absolute error (MAE) of 2.967% on the public dataset and 2.624% on the private dataset, significantly outperforming traditional calibration and non-transferred machine learning baselines. Moreover, using 25Hz PPG reduces power consumption by 40% compared to 100Hz, excluding baseline draw. Our method also attains an MAE of 3.284% in instantaneous SpO2 prediction, effectively capturing rapid fluctuations. These results demonstrate the rapid adaptation of accurate, low-power SpO2 monitoring on wearable devices without the need for clinical calibration.
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