Contactless pulse rate assessment: Results and insights for application in driving simulator
{\DJ}or{\dj}e D. Ne\v{s}kovi\'c, Kristina Stojmenova Pe\v{c}e\v{c}nik, Jaka Sodnik, Nadica Miljkovi\'c

TL;DR
This study evaluates a non-contact pulse rate estimation method using remote photoplethysmography with Eulerian Video Magnification in driving simulators, highlighting its accuracy, efficiency, and potential for driver monitoring applications.
Contribution
The paper presents an rPPG framework combining signal processing and EVM, providing insights into its effectiveness and time complexity in dynamic driving environments.
Findings
EVM slightly improves pulse rate estimation accuracy.
Mean absolute error reduced from 6.48 bpm to 5.04 bpm with EVM.
Significant age-related differences observed in pulse data.
Abstract
Remote photoplethysmography (rPPG) offers a promising solution for non-contact driver monitoring by detecting subtle blood flow-induced facial color changes from video. However, motion artifacts in dynamic driving environments remain key challenges. This study presents an rPPG framework that combines signal processing techniques before and after applying Eulerian Video Magnification (EVM) for pulse rate (PR) estimation in driving simulators. While not novel, the approach offers insights into the efficiency of the EVM method and its time complexity. We compare results of the proposed rPPG approach against reference Empatica E4 data and also compare it with existing achievements from the literature. Additionally, the possible bias of the Empatica E4 is further assessed using an independent dataset with both the Empatica E4 and the Faros 360 measurements. EVM slightly improves PR…
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Taxonomy
MethodsExtreme Value Machine · Masked autoencoder
