Design, Implementation and Evaluation of a Real-Time Remote Photoplethysmography (rPPG) Acquisition System for Non-Invasive Vital Sign Monitoring
Constantino Álvarez Casado, Sasan Sharifipour, Manuel Lage Cañellas, Nhi Nguyen, Le Nguyen, Miguel Bordallo López
原文摘要
The growing integration of smart environments and low-power computing devices, coupled with mass-market sensor technologies, is driving advancements in remote and non-contact-based physiological monitoring. However, deploying these systems in real-time on resource-constrained platforms introduces significant challenges related to scalability, interoperability, and performance. This paper presents a computationally efficient real-time remote photoplethysmography (rPPG) system, designed to extract physiological signals, such as heart rate, respiratory rate, and oxygen saturation (SpO2), from facial video streams. The system is built on the Face2PPG pipeline, which processes video frames sequentially for rPPG signal extraction and analysis, while leveraging a multithreaded architecture to manage video capture, real-time processing, network communication, and graphical user interface updates concurrently. This design targets continuous operation at 30 frames per second (FPS), with adaptive feedback through a collaborative user interface to guide optimal signal capture conditions. The network interface includes both an HTTP server for continuous video streaming and a RESTful API for on-demand vital sign retrieval. To ensure accurate performance despite limited computational resources, we use a hybrid programming model combining Functional Reactive Programming (FRP) and the Actor Model, allowing event-driven processing and efficient task parallelization. We evaluate HR accuracy on three public datasets (COHFACE, LGI-PPGI, and UBFC-RPPG), and conduct preliminary evaluations of RR on COHFACE and SpO2 on PURE. We also assess the processing time and end-to-end operation of the system on two computers, including live HR and SpO2 agreement with a medical-grade fingertip pulse oximeter across five 10-minute sessions. Our work addresses key challenges in real-time contactless biosignal monitoring.
cs.CV