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Fatigue Driving Detection Method Based on Combination of BP Neural Network and Time Cumulative Effect
Fatigue driving has always received a lot of attention, but few studies have focused on the fact that human fatigue is a cumulative process over time, and there are no models available to reflect this phenomenon. Furthermore, the problem of incorrect detection due to facial expression is still not w...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269348/ https://www.ncbi.nlm.nih.gov/pubmed/35808213 http://dx.doi.org/10.3390/s22134717 |
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author | Chen, Jian Yan, Ming Zhu, Feng Xu, Jing Li, Hai Sun, Xiaoguang |
author_facet | Chen, Jian Yan, Ming Zhu, Feng Xu, Jing Li, Hai Sun, Xiaoguang |
author_sort | Chen, Jian |
collection | PubMed |
description | Fatigue driving has always received a lot of attention, but few studies have focused on the fact that human fatigue is a cumulative process over time, and there are no models available to reflect this phenomenon. Furthermore, the problem of incorrect detection due to facial expression is still not well addressed. In this article, a model based on BP neural network and time cumulative effect was proposed to solve these problems. Experimental data were used to carry out this work and validate the proposed method. Firstly, the Adaboost algorithm was applied to detect faces, and the Kalman filter algorithm was used to trace the face movement. Then, a cascade regression tree-based method was used to detect the 68 facial landmarks and an improved method combining key points and image processing was adopted to calculate the eye aspect ratio (EAR). After that, a BP neural network model was developed and trained by selecting three characteristics: the longest period of continuous eye closure, number of yawns, and percentage of eye closure time (PERCLOS), and then the detection results without and with facial expressions were discussed and analyzed. Finally, by introducing the Sigmoid function, a fatigue detection model considering the time accumulation effect was established, and the drivers’ fatigue state was identified segment by segment through the recorded video. Compared with the traditional BP neural network model, the detection accuracies of the proposed model without and with facial expressions increased by 3.3% and 8.4%, respectively. The number of incorrect detections in the awake state also decreased obviously. The experimental results show that the proposed model can effectively filter out incorrect detections caused by facial expressions and truly reflect that driver fatigue is a time accumulating process. |
format | Online Article Text |
id | pubmed-9269348 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92693482022-07-09 Fatigue Driving Detection Method Based on Combination of BP Neural Network and Time Cumulative Effect Chen, Jian Yan, Ming Zhu, Feng Xu, Jing Li, Hai Sun, Xiaoguang Sensors (Basel) Article Fatigue driving has always received a lot of attention, but few studies have focused on the fact that human fatigue is a cumulative process over time, and there are no models available to reflect this phenomenon. Furthermore, the problem of incorrect detection due to facial expression is still not well addressed. In this article, a model based on BP neural network and time cumulative effect was proposed to solve these problems. Experimental data were used to carry out this work and validate the proposed method. Firstly, the Adaboost algorithm was applied to detect faces, and the Kalman filter algorithm was used to trace the face movement. Then, a cascade regression tree-based method was used to detect the 68 facial landmarks and an improved method combining key points and image processing was adopted to calculate the eye aspect ratio (EAR). After that, a BP neural network model was developed and trained by selecting three characteristics: the longest period of continuous eye closure, number of yawns, and percentage of eye closure time (PERCLOS), and then the detection results without and with facial expressions were discussed and analyzed. Finally, by introducing the Sigmoid function, a fatigue detection model considering the time accumulation effect was established, and the drivers’ fatigue state was identified segment by segment through the recorded video. Compared with the traditional BP neural network model, the detection accuracies of the proposed model without and with facial expressions increased by 3.3% and 8.4%, respectively. The number of incorrect detections in the awake state also decreased obviously. The experimental results show that the proposed model can effectively filter out incorrect detections caused by facial expressions and truly reflect that driver fatigue is a time accumulating process. MDPI 2022-06-22 /pmc/articles/PMC9269348/ /pubmed/35808213 http://dx.doi.org/10.3390/s22134717 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 Chen, Jian Yan, Ming Zhu, Feng Xu, Jing Li, Hai Sun, Xiaoguang Fatigue Driving Detection Method Based on Combination of BP Neural Network and Time Cumulative Effect |
title | Fatigue Driving Detection Method Based on Combination of BP Neural Network and Time Cumulative Effect |
title_full | Fatigue Driving Detection Method Based on Combination of BP Neural Network and Time Cumulative Effect |
title_fullStr | Fatigue Driving Detection Method Based on Combination of BP Neural Network and Time Cumulative Effect |
title_full_unstemmed | Fatigue Driving Detection Method Based on Combination of BP Neural Network and Time Cumulative Effect |
title_short | Fatigue Driving Detection Method Based on Combination of BP Neural Network and Time Cumulative Effect |
title_sort | fatigue driving detection method based on combination of bp neural network and time cumulative effect |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269348/ https://www.ncbi.nlm.nih.gov/pubmed/35808213 http://dx.doi.org/10.3390/s22134717 |
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