Pushing the Limit of PPG Sensing in Sedentary Conditions by Addressing Poor Skin-sensor Contact

2025-04-03

AI Summary

Poor skin–sensor contact during rest induces photoplethysmography (PPG) waveform distortion, feature degradation, and consequent bias in physiological parameter estimation. Method: This work first systematically characterizes the critical influence mechanism of contact pressure on PPG morphology. We propose CP-PPG—a contact-robust, end-to-end PPG reconstruction framework integrating customized data acquisition, a multi-stage signal processing pipeline, and a PPG-aware loss-driven deep adversarial network to accurately reconstruct distorted signals toward ideal morphology. Results: Experiments demonstrate a 40% improvement in signal fidelity (MAE = 0.09), with estimation accuracy gains of 21% for heart rate, 41–46% for heart rate variability, 6% for respiration rate, and 4–5% for blood pressure. CP-PPG significantly enhances the reliability and robustness of cardiovascular and respiratory health monitoring under resting conditions.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

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.

Problem

• Addressing poor skin-sensor contact in PPG signals
• Improving PPG signal morphology in sedentary conditions
• Enhancing accuracy of physiological monitoring via CP-PPG

Innovation

• CP-PPG transforms distorted PPG signals
• Uses deep adversarial model with custom loss
• Improves signal fidelity and downstream performance