A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness
Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI) systems have been proposed to detect driver drowsiness. However,...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/15/8/20873 |
| _version_ | 1828153765264359424 |
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| author | Gang Li Wan-Young Chung |
| author_facet | Gang Li Wan-Young Chung |
| author_sort | Gang Li |
| collection | DOAJ |
| description | Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI) systems have been proposed to detect driver drowsiness. However, detecting driver drowsiness at its early stage poses a major practical hurdle when using existing BMI systems. This study proposes a context-aware BMI system aimed to detect driver drowsiness at its early stage by enriching the EEG data with the intensity of head-movements. The proposed system is carefully designed for low-power consumption with on-chip feature extraction and low energy Bluetooth connection. Also, the proposed system is implemented using JAVA programming language as a mobile application for on-line analysis. In total, 266 datasets obtained from six subjects who participated in a one-hour monotonous driving simulation experiment were used to evaluate this system. According to a video-based reference, the proposed system obtained an overall detection accuracy of 82.71% for classifying alert and slightly drowsy events by using EEG data alone and 96.24% by using the hybrid data of head-movement and EEG. These results indicate that the combination of EEG data and head-movement contextual information constitutes a robust solution for the early detection of driver drowsiness. |
| first_indexed | 2024-04-11T22:30:10Z |
| format | Article |
| id | doaj.art-4e8507336b044a25afea08fe83325361 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:30:10Z |
| publishDate | 2015-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4e8507336b044a25afea08fe833253612022-12-22T03:59:29ZengMDPI AGSensors1424-82202015-08-01158208732089310.3390/s150820873s150820873A Context-Aware EEG Headset System for Early Detection of Driver DrowsinessGang Li0Wan-Young Chung1Department of Electronic Engineering, Pukyong National University, Busan 608-737, KoreaDepartment of Electronic Engineering, Pukyong National University, Busan 608-737, KoreaDriver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI) systems have been proposed to detect driver drowsiness. However, detecting driver drowsiness at its early stage poses a major practical hurdle when using existing BMI systems. This study proposes a context-aware BMI system aimed to detect driver drowsiness at its early stage by enriching the EEG data with the intensity of head-movements. The proposed system is carefully designed for low-power consumption with on-chip feature extraction and low energy Bluetooth connection. Also, the proposed system is implemented using JAVA programming language as a mobile application for on-line analysis. In total, 266 datasets obtained from six subjects who participated in a one-hour monotonous driving simulation experiment were used to evaluate this system. According to a video-based reference, the proposed system obtained an overall detection accuracy of 82.71% for classifying alert and slightly drowsy events by using EEG data alone and 96.24% by using the hybrid data of head-movement and EEG. These results indicate that the combination of EEG data and head-movement contextual information constitutes a robust solution for the early detection of driver drowsiness.http://www.mdpi.com/1424-8220/15/8/20873driver drowsiness detectionEEGgyroscopeslightly drowsy eventsmobile application |
| spellingShingle | Gang Li Wan-Young Chung A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness Sensors driver drowsiness detection EEG gyroscope slightly drowsy events mobile application |
| title | A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness |
| title_full | A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness |
| title_fullStr | A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness |
| title_full_unstemmed | A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness |
| title_short | A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness |
| title_sort | context aware eeg headset system for early detection of driver drowsiness |
| topic | driver drowsiness detection EEG gyroscope slightly drowsy events mobile application |
| url | http://www.mdpi.com/1424-8220/15/8/20873 |
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