AI Innovations in rPPG Systems for Driver Monitoring: Comprehensive Systematic Review and Future Prospects

Author First name, Last name, Institution

Soha G. Ahmed, United Arab Emirates University
Katrien Verbert, KU Leuven
Nazar Zaki, United Arab Emirates University
Ashraf Khalil, Zayed University
Hamad Aljassmi, United Arab Emirates University
Fady Alnajjar, United Arab Emirates University

Document Type

Article

Source of Publication

IEEE Access

Publication Date

1-1-2025

Abstract

Advanced technologies, notably camera-based systems using remote photoplethysmography (rPPG), are increasingly used in automotive safety to non-invasively monitor driver well-being and fatigue by measuring physiological metrics like heart and respiration rates. This review examines recent advancements in machine learning algorithms and signal processing for rPPG in driver monitoring. A literature search up to April 2, 2024, across major databases, identified 344 studies; 29 were analyzed in depth, focusing on: 1) rPPG signal extraction and heart rate estimation, where deep learning improved accuracy; 2) fatigue detection, showing benefits of multimodal data fusion; 3) mental state monitoring, with machine learning classifying cognitive load and distraction; and 4) emotional state monitoring and dataset development, indicating a trend toward holistic driver assessment. While deep learning has improved rPPG signal extraction, challenges remain in consistent physiological metric detection under dynamic conditions. There is also a lack of diverse population representation, especially female drivers, in datasets. The review underscores the potential of AI-enhanced camera systems to improve road safety, emphasizing the need for diverse, multimodal data integration for comprehensive monitoring.

DOI Link

10.1109/access.2025.3535540

ISSN

2169-3536

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

13

First Page

22893

Last Page

22918

Disciplines

Computer Sciences

Keywords

Automotive safety, deep learning, driver monitoring, machine learning, physiological signals, rPPG, signal processing

Scopus ID

85216950747

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Ahmed, Soha G.; Verbert, Katrien; Zaki, Nazar; Khalil, Ashraf; Aljassmi, Hamad; and Alnajjar, Fady, "AI Innovations in rPPG Systems for Driver Monitoring: Comprehensive Systematic Review and Future Prospects" (2025). All Works. 7114.
https://zuscholars.zu.ac.ae/works/7114

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series