Camera-based Vital Sign Detection in Autonomous Vehicles using Deep Learning - A Benchmark Study

Abstract

This study explores the feasibility of employing camera-based, deep learning algorithms for detecting vital signs in autonomous vehicles, with a focus on enhancing driver safety. By evaluating various remote photoplethysmography techniques in dynamic vehicular environments, challenges such as motion artifacts and varying lighting conditions were addressed. Findings suggest that machine learning models, particularly neural network based approaches, hold promise in accurately estimating heart rate and respiratory rate from video data in such settings. The study emphasizes the potential of deep learning methodologies to improve driver safety through the integration of non-invasive vital sign monitoring systems in autonomous vehicles. Future research should address dataset imbalances and broaden the benchmark scope to include additional vital signs and algorithms, while also exploring alternative methods such as optical-flow based approaches to enhance respiration rate detection.