Photoplethysmography (PPG) is a widely used non-invasive technique for monitoring cardiovascular health and various physiological parameters on consumer and medical devices. While motion artifacts are well-known challenges in dynamic settings, suboptimal skin-sensor contact in sedentary conditions - a critical issue often overlooked in existing literature - can distort PPG signal morphology, leading to the loss or shift of essential waveform features and therefore degrading sensing performance. In this work, we propose CP-PPG, a novel approach that transforms Contact Pressure-distorted PPG signals into ones with the ideal morphology. CP-PPG incorporates a novel data collection approach, a well-crafted signal processing pipeline, and an advanced deep adversarial model trained with a custom PPG-aware loss function. We validated CP-PPG through comprehensive evaluations, including 1) morphology transformation performance on our self-collected dataset, 2) downstream physiological monitoring performance on public datasets, and 3) in-the-wild performance. Extensive experiments demonstrate substantial and consistent improvements in signal fidelity (Mean Absolute Error: 0.09, 40% improvement over the original signal) as well as downstream performance across all evaluations in Heart Rate (HR), Heart Rate Variability (HRV), Respiration Rate (RR), and Blood Pressure (BP) estimation (on average, 21% improvement in HR; 41-46% in HRV; 6% in RR; and 4-5% in BP). These findings highlight the critical importance of addressing skin-sensor contact issues for accurate and dependable PPG-based physiological monitoring. Furthermore, CP-PPG can serve as a generic, plug-in API to enhance PPG signal quality.

翻译：光电容积脉搏波描记法（PPG）是一种广泛应用于消费级和医疗设备上监测心血管健康及多种生理参数的无创技术。虽然运动伪影是动态场景中众所周知的挑战，但静息状态下皮肤-传感器接触不良——这一现有文献中常被忽视的关键问题——会扭曲PPG信号的形态，导致关键波形特征的丢失或偏移，从而降低传感性能。在本工作中，我们提出了CP-PPG，一种将接触压力扭曲的PPG信号转化为具有理想形态信号的新方法。CP-PPG包含一种新颖的数据采集方法、精心设计的信号处理流程，以及一个采用定制化PPG感知损失函数训练的先进深度对抗模型。我们通过全面的评估验证了CP-PPG，包括：1）在我们自采集数据集上的形态转换性能，2）在公共数据集上的下游生理监测性能，以及3）在真实环境中的性能。大量实验表明，在信号保真度（平均绝对误差：0.09，较原始信号提升40%）以及所有评估中的下游性能（心率（HR）、心率变异性（HRV）、呼吸频率（RR）和血压（BP）估计）方面均取得了显著且一致的改进（平均而言，HR提升21%；HRV提升41-46%；RR提升6%；BP提升4-5%）。这些发现凸显了解决皮肤-传感器接触问题对于实现准确可靠的基于PPG的生理监测至关重要。此外，CP-PPG可以作为一个通用的即插即用API来提升PPG信号质量。