Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion
Abstract Many traffic accidents occur nowadays as a result of drivers not paying enough attention or being vigilant. We call this driver sleepiness. This results in numerous unfavourable circumstances that negatively impact people’s life. The identification of driver fatigue and the appropriate hand...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-89479-y |
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| author | Meenakshi Malik Preeti Sharma Gurpreet Kaur Punj Supreet Singh Fikreselam Gared |
| author_facet | Meenakshi Malik Preeti Sharma Gurpreet Kaur Punj Supreet Singh Fikreselam Gared |
| author_sort | Meenakshi Malik |
| collection | DOAJ |
| description | Abstract Many traffic accidents occur nowadays as a result of drivers not paying enough attention or being vigilant. We call this driver sleepiness. This results in numerous unfavourable circumstances that negatively impact people’s life. The identification of driver fatigue and the appropriate handling of such information is the primary objective of this study. Ongoing developments in AI (artificial intelligence) as well as ML (machine learning) within ADAS (Advanced Driver Assistance Systems) have made the application of Internet-of-Things (IoT) technology in driver action recognition necessary. These advancements are dramatically changing the driving experience. This study suggests a novel method for machine learning-based automatic driving change-based drowsiness detection. In this instance, the multi-body sensor detects the driver’s EEG signal and gathers information for brain activity analysis. The wavelet time frequency transform model has been used to examine this signal in order to classify patterns of brain activity. A multi-layer convolutional programmed transfer VGG-16 neural network was then used to classify this examined pattern. This classified signal will cause the automatic driving mode to change. In terms of prediction accuracy, sensitivity, specificity, RMSE, ROC, experimental analysis has been performed for a variety of EEG signal datasets. The goal of this work is to reduce the risks that come with driving while drowsy which will improve road safety and reduce incidents that are related to fatigue. |
| format | Article |
| id | doaj-art-22558e97f9f443c0b28215e30d22bb7d |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-22558e97f9f443c0b28215e30d22bb7d2025-08-20T03:01:37ZengNature PortfolioScientific Reports2045-23222025-03-0115111310.1038/s41598-025-89479-yMulti-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversionMeenakshi Malik0Preeti Sharma1Gurpreet Kaur Punj2Supreet Singh3Fikreselam Gared4Department of CSE, BML Munjal UniversityChitkara University Institute of Engineering and Technology, Chitkara UniversityComputer Science Engineering Department, Punjabi UniversitySchool of Computer Science, UPESDepartment of Electrical and Computer Engineering, Bahir Dar UniversityAbstract Many traffic accidents occur nowadays as a result of drivers not paying enough attention or being vigilant. We call this driver sleepiness. This results in numerous unfavourable circumstances that negatively impact people’s life. The identification of driver fatigue and the appropriate handling of such information is the primary objective of this study. Ongoing developments in AI (artificial intelligence) as well as ML (machine learning) within ADAS (Advanced Driver Assistance Systems) have made the application of Internet-of-Things (IoT) technology in driver action recognition necessary. These advancements are dramatically changing the driving experience. This study suggests a novel method for machine learning-based automatic driving change-based drowsiness detection. In this instance, the multi-body sensor detects the driver’s EEG signal and gathers information for brain activity analysis. The wavelet time frequency transform model has been used to examine this signal in order to classify patterns of brain activity. A multi-layer convolutional programmed transfer VGG-16 neural network was then used to classify this examined pattern. This classified signal will cause the automatic driving mode to change. In terms of prediction accuracy, sensitivity, specificity, RMSE, ROC, experimental analysis has been performed for a variety of EEG signal datasets. The goal of this work is to reduce the risks that come with driving while drowsy which will improve road safety and reduce incidents that are related to fatigue.https://doi.org/10.1038/s41598-025-89479-yAutomatic drivingDrowsiness detectionMachine learning modelEEG signalMulti-body sensorFrequency transform model |
| spellingShingle | Meenakshi Malik Preeti Sharma Gurpreet Kaur Punj Supreet Singh Fikreselam Gared Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion Scientific Reports Automatic driving Drowsiness detection Machine learning model EEG signal Multi-body sensor Frequency transform model |
| title | Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion |
| title_full | Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion |
| title_fullStr | Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion |
| title_full_unstemmed | Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion |
| title_short | Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion |
| title_sort | multi body sensor based drowsiness detection using convolutional programmed transfer vgg 16 neural network with automatic driving mode conversion |
| topic | Automatic driving Drowsiness detection Machine learning model EEG signal Multi-body sensor Frequency transform model |
| url | https://doi.org/10.1038/s41598-025-89479-y |
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