Cargando…
System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures
The amount of road accidents caused by driver drowsiness is one of the world’s major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year. As a result, it is critical to prevent catastrophic acci...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920860/ https://www.ncbi.nlm.nih.gov/pubmed/36772333 http://dx.doi.org/10.3390/s23031292 |
_version_ | 1784887173634326528 |
---|---|
author | Bajaj, Jaspreet Singh Kumar, Naveen Kaushal, Rajesh Kumar Gururaj, H. L. Flammini, Francesco Natarajan, Rajesh |
author_facet | Bajaj, Jaspreet Singh Kumar, Naveen Kaushal, Rajesh Kumar Gururaj, H. L. Flammini, Francesco Natarajan, Rajesh |
author_sort | Bajaj, Jaspreet Singh |
collection | PubMed |
description | The amount of road accidents caused by driver drowsiness is one of the world’s major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year. As a result, it is critical to prevent catastrophic accidents and reduce the financial burden on society caused by driver drowsiness. The research community has primarily focused on two approaches to identify driver drowsiness during the last decade: intrusive and non-intrusive. The intrusive approach includes physiological measures, and the non-intrusive approach includes vehicle-based and behavioral measures. In an intrusive approach, sensors are used to detect driver drowsiness by placing them on the driver’s body, whereas in a non-intrusive approach, a camera is used for drowsiness detection by identifying yawning patterns, eyelid movement and head inclination. Noticeably, most research has been conducted in driver drowsiness detection methods using only single measures that failed to produce good outcomes. Furthermore, these measures were only functional in certain conditions. This paper proposes a model that combines the two approaches, non-intrusive and intrusive, to detect driver drowsiness. Behavioral measures as a non-intrusive approach and sensor-based physiological measures as an intrusive approach are combined to detect driver drowsiness. The proposed hybrid model uses AI-based Multi-Task Cascaded Convolutional Neural Networks (MTCNN) as a behavioral measure to recognize the driver’s facial features, and the Galvanic Skin Response (GSR) sensor as a physiological measure to collect the skin conductance of the driver that helps to increase the overall accuracy. Furthermore, the model’s efficacy has been computed in a simulated environment. The outcome shows that the proposed hybrid model is capable of identifying the transition from awake to a drowsy state in the driver in all conditions with the efficacy of 91%. |
format | Online Article Text |
id | pubmed-9920860 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99208602023-02-12 System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures Bajaj, Jaspreet Singh Kumar, Naveen Kaushal, Rajesh Kumar Gururaj, H. L. Flammini, Francesco Natarajan, Rajesh Sensors (Basel) Article The amount of road accidents caused by driver drowsiness is one of the world’s major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year. As a result, it is critical to prevent catastrophic accidents and reduce the financial burden on society caused by driver drowsiness. The research community has primarily focused on two approaches to identify driver drowsiness during the last decade: intrusive and non-intrusive. The intrusive approach includes physiological measures, and the non-intrusive approach includes vehicle-based and behavioral measures. In an intrusive approach, sensors are used to detect driver drowsiness by placing them on the driver’s body, whereas in a non-intrusive approach, a camera is used for drowsiness detection by identifying yawning patterns, eyelid movement and head inclination. Noticeably, most research has been conducted in driver drowsiness detection methods using only single measures that failed to produce good outcomes. Furthermore, these measures were only functional in certain conditions. This paper proposes a model that combines the two approaches, non-intrusive and intrusive, to detect driver drowsiness. Behavioral measures as a non-intrusive approach and sensor-based physiological measures as an intrusive approach are combined to detect driver drowsiness. The proposed hybrid model uses AI-based Multi-Task Cascaded Convolutional Neural Networks (MTCNN) as a behavioral measure to recognize the driver’s facial features, and the Galvanic Skin Response (GSR) sensor as a physiological measure to collect the skin conductance of the driver that helps to increase the overall accuracy. Furthermore, the model’s efficacy has been computed in a simulated environment. The outcome shows that the proposed hybrid model is capable of identifying the transition from awake to a drowsy state in the driver in all conditions with the efficacy of 91%. MDPI 2023-01-23 /pmc/articles/PMC9920860/ /pubmed/36772333 http://dx.doi.org/10.3390/s23031292 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bajaj, Jaspreet Singh Kumar, Naveen Kaushal, Rajesh Kumar Gururaj, H. L. Flammini, Francesco Natarajan, Rajesh System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures |
title | System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures |
title_full | System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures |
title_fullStr | System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures |
title_full_unstemmed | System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures |
title_short | System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures |
title_sort | system and method for driver drowsiness detection using behavioral and sensor-based physiological measures |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920860/ https://www.ncbi.nlm.nih.gov/pubmed/36772333 http://dx.doi.org/10.3390/s23031292 |
work_keys_str_mv | AT bajajjaspreetsingh systemandmethodfordriverdrowsinessdetectionusingbehavioralandsensorbasedphysiologicalmeasures AT kumarnaveen systemandmethodfordriverdrowsinessdetectionusingbehavioralandsensorbasedphysiologicalmeasures AT kaushalrajeshkumar systemandmethodfordriverdrowsinessdetectionusingbehavioralandsensorbasedphysiologicalmeasures AT gururajhl systemandmethodfordriverdrowsinessdetectionusingbehavioralandsensorbasedphysiologicalmeasures AT flamminifrancesco systemandmethodfordriverdrowsinessdetectionusingbehavioralandsensorbasedphysiologicalmeasures AT natarajanrajesh systemandmethodfordriverdrowsinessdetectionusingbehavioralandsensorbasedphysiologicalmeasures |