Design, Implementation and Evaluation of a Real-Time Remote Photoplethysmography (rPPG) Acquisition System for Non-Invasive Vital Sign Monitoring

TL;DR

This paper introduces a real-time, low-power compatible remote photoplethysmography system that accurately extracts vital signs from facial videos, addressing scalability and performance challenges in non-contact health monitoring.

Contribution

It presents a novel real-time rPPG system optimized for low-power devices using a hybrid programming model, with a multithreaded architecture and adaptive feedback for reliable vital sign monitoring.

Findings

Achieves 30 fps real-time processing on resource-constrained devices.

Demonstrates accurate extraction of HR, RR, and SpO2 from facial videos.

Ensures robustness and minimal computational overhead in real-world scenarios.

Abstract

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 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 real-time remote photoplethysmography (rPPG) system optimized for low-power devices, designed to extract physiological signals, such as heart rate (HR), respiratory rate (RR), 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 (GUI) updates concurrently. This design ensures continuous, reliable 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 the limitations of low-power devices, we use a hybrid programming model combining Functional Reactive Programming (FRP) and the Actor Model, allowing event-driven processing and efficient task parallelization. The system is evaluated under real-time constraints, demonstrating robustness while minimizing computational overhead. Our work addresses key challenges in real-time biosignal monitoring, offering practical solutions for optimizing performance in modern healthcare and human-computer interaction applications.