Development and implementation of a driver hypovigilance detection system based on EEG using DWT

In most real-time scenarios, it is highly essential to evaluate the level of hypovigilance in drivers, pilots, security guards and sportsmen to ensure efficient performance in their work. Driver hypovigilance is one of the major causes for road accidents. Drowsiness and distraction are two major com...

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Main Author:	Mousa Kadhim, Wali
Format:	Thesis
Language:	English
Subjects:	Hypovigilance Hypovigilance detection system Driver hypovigilance Electroencephalogram (EEG) signal analysis Hypovigilance detection system > Design and construction
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31528/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31528/2/Full%20text.pdf
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id	my-unimap-31528
record_format	uketd_dc
spelling	my-unimap-315282014-01-28T03:24:05Z Development and implementation of a driver hypovigilance detection system based on EEG using DWT Mousa Kadhim, Wali In most real-time scenarios, it is highly essential to evaluate the level of hypovigilance in drivers, pilots, security guards and sportsmen to ensure efficient performance in their work. Driver hypovigilance is one of the major causes for road accidents. Drowsiness and distraction are two major components of hypovigilance. Most real time detection systems use the conventional classifier based approach to distinguish different levels and proposed the measurement index to differentiate lower numbers of hypovigilance levels (such as drowsy versus awake, distraction versus neutral, etc.). Furthermore, existing detection systems are bulky and costly. In this thesis electroencephalogram (EEG) signal analysis in the time-frequency domain with application to driver hypovigilance recognition is developed. Five stimuli are considered for devolving an intelligent hypovigilance detection system. 14 wireless multi channel EEG are placed over the entire scalp through international 10-20 system. The EEG dataset is developed with 50 subjects (43 males and 7 females) and the discrete hypovigilance states (neutral, low distraction, medium distraction, high distraction, awake, drowsy, high drowsy, sleep stage 1) are evoked by using audio and visual stimuli (media player, GPS, mental think, SMS, and driving for 1hour). Distraction, sleepy, and hypovigilance indices were derived from EEG frequency bands; delta (0-4Hz), theta (4-8Hz), alpha (8-12Hz), and beta (14-32Hz) using hybridization of Discrete Wavelet Transform (DWT) and FFT. Two statistical features (Spectral centroid (SC), Power Spectral Density (PSD)) derived from EEG frequency bands using different wavelets (db4, db8, sym8, and coif5) are used to classify the hypovigilance levels using three classifiers namely; Probabilistic Neural Network (PNN), K Nearest Neighbour (KNN) and subtractive fuzzy classifier. As a result of this study, the average of distraction and sleepy index detection rate were 88.75% and 85% respectively, both based on db4. On the other hand, subtractive fuzzy classifier based distraction and drowsiness achieves maximum classification rate of 79.21% based on sym8, and 84.41% based on db4, respectively. The embedded system (TS7800) has been used in this research as real time hypovigilance detection system based on hybridization of discrete wavelet transform and fast Fourier transform. The conventional filter bank method had been investigated and compared with hybrid method. The Results of this research indicated that db4 based hypovigilance detection system gave best average detection rate using index method. While classification method showed that sym8 and db4 gave high accuracy results when they were applied for features extraction of distraction and drowsiness states, respectively. The output of this thesis is the newly distraction, drowsiness, and hypovigilance indices obtained by hybridization of DWT and FFT, in addition to hardware real time detection system based on embedded system. Universiti Malaysia Perlis (UniMAP) 2013 Thesis en http://dspace.unimap.edu.my:80/dspace/handle/123456789/31528 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31528/1/Page%201-24.pdf e0fb6faaef9b706704f7d0eedaf573d8 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31528/2/Full%20text.pdf eb360c5edb7afac702c23a513899fbbd http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31528/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Hypovigilance Hypovigilance detection system Driver hypovigilance Electroencephalogram (EEG) signal analysis Hypovigilance detection system -- Design and construction School of Computer and Communication Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
topic	Hypovigilance Hypovigilance detection system Driver hypovigilance Electroencephalogram (EEG) signal analysis Hypovigilance detection system -- Design and construction
spellingShingle	Hypovigilance Hypovigilance detection system Driver hypovigilance Electroencephalogram (EEG) signal analysis Hypovigilance detection system -- Design and construction Mousa Kadhim, Wali Development and implementation of a driver hypovigilance detection system based on EEG using DWT
description	In most real-time scenarios, it is highly essential to evaluate the level of hypovigilance in drivers, pilots, security guards and sportsmen to ensure efficient performance in their work. Driver hypovigilance is one of the major causes for road accidents. Drowsiness and distraction are two major components of hypovigilance. Most real time detection systems use the conventional classifier based approach to distinguish different levels and proposed the measurement index to differentiate lower numbers of hypovigilance levels (such as drowsy versus awake, distraction versus neutral, etc.). Furthermore, existing detection systems are bulky and costly. In this thesis electroencephalogram (EEG) signal analysis in the time-frequency domain with application to driver hypovigilance recognition is developed. Five stimuli are considered for devolving an intelligent hypovigilance detection system. 14 wireless multi channel EEG are placed over the entire scalp through international 10-20 system. The EEG dataset is developed with 50 subjects (43 males and 7 females) and the discrete hypovigilance states (neutral, low distraction, medium distraction, high distraction, awake, drowsy, high drowsy, sleep stage 1) are evoked by using audio and visual stimuli (media player, GPS, mental think, SMS, and driving for 1hour). Distraction, sleepy, and hypovigilance indices were derived from EEG frequency bands; delta (0-4Hz), theta (4-8Hz), alpha (8-12Hz), and beta (14-32Hz) using hybridization of Discrete Wavelet Transform (DWT) and FFT. Two statistical features (Spectral centroid (SC), Power Spectral Density (PSD)) derived from EEG frequency bands using different wavelets (db4, db8, sym8, and coif5) are used to classify the hypovigilance levels using three classifiers namely; Probabilistic Neural Network (PNN), K Nearest Neighbour (KNN) and subtractive fuzzy classifier. As a result of this study, the average of distraction and sleepy index detection rate were 88.75% and 85% respectively, both based on db4. On the other hand, subtractive fuzzy classifier based distraction and drowsiness achieves maximum classification rate of 79.21% based on sym8, and 84.41% based on db4, respectively. The embedded system (TS7800) has been used in this research as real time hypovigilance detection system based on hybridization of discrete wavelet transform and fast Fourier transform. The conventional filter bank method had been investigated and compared with hybrid method. The Results of this research indicated that db4 based hypovigilance detection system gave best average detection rate using index method. While classification method showed that sym8 and db4 gave high accuracy results when they were applied for features extraction of distraction and drowsiness states, respectively. The output of this thesis is the newly distraction, drowsiness, and hypovigilance indices obtained by hybridization of DWT and FFT, in addition to hardware real time detection system based on embedded system.
format	Thesis
author	Mousa Kadhim, Wali
author_facet	Mousa Kadhim, Wali
author_sort	Mousa Kadhim, Wali
title	Development and implementation of a driver hypovigilance detection system based on EEG using DWT
title_short	Development and implementation of a driver hypovigilance detection system based on EEG using DWT
title_full	Development and implementation of a driver hypovigilance detection system based on EEG using DWT
title_fullStr	Development and implementation of a driver hypovigilance detection system based on EEG using DWT
title_full_unstemmed	Development and implementation of a driver hypovigilance detection system based on EEG using DWT
title_sort	development and implementation of a driver hypovigilance detection system based on eeg using dwt
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Computer and Communication Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31528/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31528/2/Full%20text.pdf
_version_	1747836791922098176

Development and implementation of a driver hypovigilance detection system based on EEG using DWT

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