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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...

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Autores principales: Chen, Jian, Yan, Ming, Zhu, Feng, Xu, Jing, Li, Hai, Sun, Xiaoguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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.
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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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