AI Innovations in rPPG Systems for Driver Monitoring: Comprehensive Systematic Review and Future Prospects
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 advancemen...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10856104/ |
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| author | Soha G. Ahmed Katrien Verbert Nazar Zaki Ashraf Khalil Hamad Aljassmi Fady Alnajjar |
| author_facet | Soha G. Ahmed Katrien Verbert Nazar Zaki Ashraf Khalil Hamad Aljassmi Fady Alnajjar |
| author_sort | Soha G. Ahmed |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-66714034767143768074c523cabd674b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-66714034767143768074c523cabd674b2025-02-07T00:01:31ZengIEEEIEEE Access2169-35362025-01-0113228932291810.1109/ACCESS.2025.353554010856104AI Innovations in rPPG Systems for Driver Monitoring: Comprehensive Systematic Review and Future ProspectsSoha G. Ahmed0https://orcid.org/0000-0002-4912-4819Katrien Verbert1https://orcid.org/0000-0001-6699-7710Nazar Zaki2https://orcid.org/0000-0002-6259-9843Ashraf Khalil3https://orcid.org/0000-0003-1584-8525Hamad Aljassmi4https://orcid.org/0000-0002-4230-4067Fady Alnajjar5https://orcid.org/0000-0001-6102-3765College of Information Technology, UAE University, Al Ain, Abu Dhabi, United Arab EmiratesDepartment of Computer Science, KU Leuven University, Flemish Brabant, Leuven, BelgiumCollege of Information Technology, UAE University, Al Ain, Abu Dhabi, United Arab EmiratesCollege of Technological Innovation, Zayed University, Abu Dhabi Campus, Abu Dhabi, United Arab EmiratesEmirates Center for Mobility Research, UAE University, Al Ain, Abu Dhabi, United Arab EmiratesCollege of Information Technology, UAE University, Al Ain, Abu Dhabi, United Arab EmiratesAdvanced 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.https://ieeexplore.ieee.org/document/10856104/Automotive safetydeep learningdriver monitoringmachine learningphysiological signalsrPPG |
| spellingShingle | Soha G. Ahmed Katrien Verbert Nazar Zaki Ashraf Khalil Hamad Aljassmi Fady Alnajjar AI Innovations in rPPG Systems for Driver Monitoring: Comprehensive Systematic Review and Future Prospects IEEE Access Automotive safety deep learning driver monitoring machine learning physiological signals rPPG |
| title | AI Innovations in rPPG Systems for Driver Monitoring: Comprehensive Systematic Review and Future Prospects |
| title_full | AI Innovations in rPPG Systems for Driver Monitoring: Comprehensive Systematic Review and Future Prospects |
| title_fullStr | AI Innovations in rPPG Systems for Driver Monitoring: Comprehensive Systematic Review and Future Prospects |
| title_full_unstemmed | AI Innovations in rPPG Systems for Driver Monitoring: Comprehensive Systematic Review and Future Prospects |
| title_short | AI Innovations in rPPG Systems for Driver Monitoring: Comprehensive Systematic Review and Future Prospects |
| title_sort | ai innovations in rppg systems for driver monitoring comprehensive systematic review and future prospects |
| topic | Automotive safety deep learning driver monitoring machine learning physiological signals rPPG |
| url | https://ieeexplore.ieee.org/document/10856104/ |
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