Authors : Barot Raj Dipakkumar; Sathiya Nishit Bharatbhai; Sumara Vijay Rameshbhai; Japan Mavani

Volume/Issue : Volume 11 - 2026, Issue 1 - January

Google Scholar : https://tinyurl.com/mrywvnnk

Scribd : https://tinyurl.com/emmwz7aa

DOI : https://doi.org/10.38124/ijisrt/26jan448

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This paper presents PulmoAI, a state-of-the-art implementation of Remote Photoplethysmography (rPPG) designed to democratize health monitoring. By transforming standard consumer webcams into medical diagnostic devices, PulmoAI extracts physiological signals—specifically Heart Rate (HR), Oxygen Saturation (SpO2), Respiratory Rate (RR), and Heart Rate Variability (HRV)—from facial video without physical contact. The system leverages the rPPG-Toolbox, utilizing 3D Convolutional Neural Networks (PhysNet) to achieve clinical-grade accuracy (MAE ~0.8 BPM). Furthermore, PulmoAI integrates a novel Medical Expert System based on the National Early Warning Score (N.E.W.S.) to detect early signs of COPD, Asthma, and Pneumonia. This paper details the multi-tiered "Full-Proof" architecture that ensures reliability across varying computational environments, ranging from high-end GPU servers to standard CPUs.

References :

1. Yu, Z., et al. "Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks." (PhysNet).
2. Chen, W., and McDuff, D. "DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks."
3. Wang, W., et al. "Algorithmic Principles of Remote PPG." (POS/CHROM methodologies).
4. Royal College of Physicians. "National Early Warning Score (NEWS) 2."