Advances in Pulse Rate Variability (PRV) Monitoring with rPPG: Insights from Unsupervised Methods

Abstract

Remote photoplethysmography (rPPG) emerges as a non-invasive alternative for pulse and pulse rate variability (PRV) measurement, eliminating the need for direct skin contact. This approach is particularly suitable for applications where wearable sensors are impractical, such as the automotive sector, where accurate and robust PRV monitoring is essential to enhance driver safety by providing real-time insights. This study evaluates the accuracy and robustness of rPPG signal extraction using the Freyja/IBV-Dataset, which comprises 73 participants with diverse intrinsic factors, such as age, body mass index (BMI), and skin phototypes, as well as extrinsic conditions, including varying lighting and distances. Seven rPPG algorithms (GREEN, POS, CHROM, ICA, FastICA, PVB, and LGI), selected for their established efficacy in handling environmental variations, were compared against electrocardiogram (ECG) as the reference standard. The findings reveal that the mean normal-to-normal interval (meanNNI) demonstrates the greatest robustness when estimated using ICA and FastICA, which achieved consistently low mean absolute errors (MAE) even under challenging conditions such as reduced lighting and increased distance. However, the estimation of the standard deviation of normal-to-normal intervals (SDNN), a parameter sensitive to noise and environmental conditions, showed higher errors. These discrepancies are attributed to intrinsic differences between mechanical (rPPG) and electrical (ECG) signals, disparities in sampling frequencies between devices, and environmental influences. This study highlights the need to optimize rPPG signal extraction and processing techniques to improve the accuracy and robustness of PRV parameter estimation. Future research should focus on increasing the image sampling rate, exploring PPG measurements closer to the face, and employing advanced artificial intelligence (AI) methods to adapt algorithms for challenging conditions, such as diverse skin phototypes and complex environmental settings.

Keywords: Pulse Rate Variability (PRV), Remote Photoplethysmography (rPPG), Unsupervised Extraction Methods, Automotive Safety, Driver Monitoring, Health Monitoring, Physiological State

DOI: 10.54941/ahfe1005941