Cargando…

Drowsiness Detection System Based on PERCLOS and Facial Physiological Signal †

Accidents caused by fatigue occur frequently, and numerous scholars have devoted tremendous efforts to investigate methods to reduce accidents caused by fatigued driving. Accordingly, the assessment of the spirit status of the driver through the eyes blinking frequency and the measurement of physiol...

Descripción completa

Detalles Bibliográficos
Autores principales: Chang, Robert Chen-Hao, Wang, Chia-Yu, Chen, Wei-Ting, Chiu, Cheng-Di
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9323611/
https://www.ncbi.nlm.nih.gov/pubmed/35891065
http://dx.doi.org/10.3390/s22145380
_version_ 1784756594283642880
author Chang, Robert Chen-Hao
Wang, Chia-Yu
Chen, Wei-Ting
Chiu, Cheng-Di
author_facet Chang, Robert Chen-Hao
Wang, Chia-Yu
Chen, Wei-Ting
Chiu, Cheng-Di
author_sort Chang, Robert Chen-Hao
collection PubMed
description Accidents caused by fatigue occur frequently, and numerous scholars have devoted tremendous efforts to investigate methods to reduce accidents caused by fatigued driving. Accordingly, the assessment of the spirit status of the driver through the eyes blinking frequency and the measurement of physiological signals have emerged as effective methods. In this study, a drowsiness detection system is proposed to combine the detection of LF/HF ratio from heart rate variability (HRV) of photoplethysmographic imaging (PPGI) and percentage of eyelid closure over the pupil over time (PERCLOS), and to utilize the advantages of both methods to improve the accuracy and robustness of drowsiness detection. The proposed algorithm performs three functions, including LF/HF ratio from HRV status judgment, eye state detection, and drowsiness judgment. In addition, this study utilized a near-infrared webcam to obtain a facial image to achieve non-contact measurement, alleviate the inconvenience of using a contact wearable device, and for use in a dark environment. Furthermore, we selected the appropriate RGB channel under different light sources to obtain LF/HF ratio from HRV of PPGI. The main drowsiness judgment basis of the proposed drowsiness detection system is the use of algorithm to obtain sympathetic/parasympathetic nervous balance index and percentage of eyelid closure. In the experiment, there are 10 awake samples and 30 sleepy samples. The sensitivity is 88.9%, the specificity is 93.5%, the positive predictive value is 80%, and the system accuracy is 92.5%. In addition, an electroencephalography signal was used as a contrast to validate the reliability of the proposed method.
format Online
Article
Text
id pubmed-9323611
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-93236112022-07-27 Drowsiness Detection System Based on PERCLOS and Facial Physiological Signal † Chang, Robert Chen-Hao Wang, Chia-Yu Chen, Wei-Ting Chiu, Cheng-Di Sensors (Basel) Article Accidents caused by fatigue occur frequently, and numerous scholars have devoted tremendous efforts to investigate methods to reduce accidents caused by fatigued driving. Accordingly, the assessment of the spirit status of the driver through the eyes blinking frequency and the measurement of physiological signals have emerged as effective methods. In this study, a drowsiness detection system is proposed to combine the detection of LF/HF ratio from heart rate variability (HRV) of photoplethysmographic imaging (PPGI) and percentage of eyelid closure over the pupil over time (PERCLOS), and to utilize the advantages of both methods to improve the accuracy and robustness of drowsiness detection. The proposed algorithm performs three functions, including LF/HF ratio from HRV status judgment, eye state detection, and drowsiness judgment. In addition, this study utilized a near-infrared webcam to obtain a facial image to achieve non-contact measurement, alleviate the inconvenience of using a contact wearable device, and for use in a dark environment. Furthermore, we selected the appropriate RGB channel under different light sources to obtain LF/HF ratio from HRV of PPGI. The main drowsiness judgment basis of the proposed drowsiness detection system is the use of algorithm to obtain sympathetic/parasympathetic nervous balance index and percentage of eyelid closure. In the experiment, there are 10 awake samples and 30 sleepy samples. The sensitivity is 88.9%, the specificity is 93.5%, the positive predictive value is 80%, and the system accuracy is 92.5%. In addition, an electroencephalography signal was used as a contrast to validate the reliability of the proposed method. MDPI 2022-07-19 /pmc/articles/PMC9323611/ /pubmed/35891065 http://dx.doi.org/10.3390/s22145380 Text en © 2022 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
Chang, Robert Chen-Hao
Wang, Chia-Yu
Chen, Wei-Ting
Chiu, Cheng-Di
Drowsiness Detection System Based on PERCLOS and Facial Physiological Signal †
title Drowsiness Detection System Based on PERCLOS and Facial Physiological Signal †
title_full Drowsiness Detection System Based on PERCLOS and Facial Physiological Signal †
title_fullStr Drowsiness Detection System Based on PERCLOS and Facial Physiological Signal †
title_full_unstemmed Drowsiness Detection System Based on PERCLOS and Facial Physiological Signal †
title_short Drowsiness Detection System Based on PERCLOS and Facial Physiological Signal †
title_sort drowsiness detection system based on perclos and facial physiological signal †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9323611/
https://www.ncbi.nlm.nih.gov/pubmed/35891065
http://dx.doi.org/10.3390/s22145380
work_keys_str_mv AT changrobertchenhao drowsinessdetectionsystembasedonperclosandfacialphysiologicalsignal
AT wangchiayu drowsinessdetectionsystembasedonperclosandfacialphysiologicalsignal
AT chenweiting drowsinessdetectionsystembasedonperclosandfacialphysiologicalsignal
AT chiuchengdi drowsinessdetectionsystembasedonperclosandfacialphysiologicalsignal