Electrophysiological Degrading Correlates For Driving Attention Loss Threshold Determination

Statistics by Malaysian Institute of Road Safety Research (MIROS) showed that attention loss significantly lead to road accidents. Hence, the area of research on attention detection for driver safety is becoming more important. There have been a number of studies that displayed the possibility of id...

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Main Author:	Mohd Nasir., Haslinah
Format:	Thesis
Language:	English English
Published:	2019
Subjects:	Q Science (General) QP Physiology
Online Access:	http://eprints.utem.edu.my/id/eprint/24512/1/Electrophysiological%20Degrading%20Correlates%20For%20Driving%20Attention%20Loss%20Threshold%20Determination.pdf http://eprints.utem.edu.my/id/eprint/24512/2/Electrophysiological%20Degrading%20Correlates%20For%20Driving%20Attention%20Loss%20Threshold%20Determination.pdf
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record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
topic	Q Science (General) QP Physiology
spellingShingle	Q Science (General) QP Physiology Mohd Nasir., Haslinah Electrophysiological Degrading Correlates For Driving Attention Loss Threshold Determination
description	Statistics by Malaysian Institute of Road Safety Research (MIROS) showed that attention loss significantly lead to road accidents. Hence, the area of research on attention detection for driver safety is becoming more important. There have been a number of studies that displayed the possibility of identifying drivers’ attention using electroencephalography (EEG) signal. The studies obtained the Event Related Potential (ERP) waveform from a small pool of samples. However, the data obtained were insufficient to significantly characterize attentiveness and inattentiveness due to the unique characteristic of each individual. Therefore, the aim of this research is to define the attentiveness state of each subject from large number of samples in controlled parameters to minimize the variability gap of the ERP peak between each individual. The experiment has been conducted using driving simulator to obtain the EEG data from two groups of subjects which were categorized as attentive and inattentive state by using two distinct stimulations i.e., listening to radio and no stimulation. The obtained results show significant boundary and similarity patterns for the level of attentiveness in both groups. Due to these patterns, a hybrid mean-fuzzy (HMF) technique was proposed to analyze the peak of N170 ERP decrement value versus the accident score based on the driving performance and attention threshold was determined accordingly. Three levels of attention namely ‘attentive’, ‘the beginning of inattentiveness’ and ‘inattentive’ state were presented within a new framework scale in the form of a fish bone diagram known as Attention Degradation Scale (ADS). In order to validate the feasibility of the proposed ADS for both groups, the analysis of the data has been done with and without ADS. Based on the outcome, 52% of the subjects were detected as attentive whilst 56% were in inattentive state which is significant as the percentage obtained with ADS was more than without it. Finally, a prototype application has been implemented to prove the theoretical data of attention level prediction. The prototype has successfully warned the subjects of potential accidents whenever the attention level was below the threshold value. Therefore, the findings of this research can be a promising foundation for alarm system which based on attention recognition technique that potentially would be able to reduce road accidents specifically with the proposed ADS.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohd Nasir., Haslinah
author_facet	Mohd Nasir., Haslinah
author_sort	Mohd Nasir., Haslinah
title	Electrophysiological Degrading Correlates For Driving Attention Loss Threshold Determination
title_short	Electrophysiological Degrading Correlates For Driving Attention Loss Threshold Determination
title_full	Electrophysiological Degrading Correlates For Driving Attention Loss Threshold Determination
title_fullStr	Electrophysiological Degrading Correlates For Driving Attention Loss Threshold Determination
title_full_unstemmed	Electrophysiological Degrading Correlates For Driving Attention Loss Threshold Determination
title_sort	electrophysiological degrading correlates for driving attention loss threshold determination
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Electronic and Computer Engineering
publishDate	2019
url	http://eprints.utem.edu.my/id/eprint/24512/1/Electrophysiological%20Degrading%20Correlates%20For%20Driving%20Attention%20Loss%20Threshold%20Determination.pdf http://eprints.utem.edu.my/id/eprint/24512/2/Electrophysiological%20Degrading%20Correlates%20For%20Driving%20Attention%20Loss%20Threshold%20Determination.pdf
_version_	1747834071790125056
spelling	my-utem-ep.245122021-10-05T10:28:43Z Electrophysiological Degrading Correlates For Driving Attention Loss Threshold Determination 2019 Mohd Nasir., Haslinah Q Science (General) QP Physiology Statistics by Malaysian Institute of Road Safety Research (MIROS) showed that attention loss significantly lead to road accidents. Hence, the area of research on attention detection for driver safety is becoming more important. There have been a number of studies that displayed the possibility of identifying drivers’ attention using electroencephalography (EEG) signal. The studies obtained the Event Related Potential (ERP) waveform from a small pool of samples. However, the data obtained were insufficient to significantly characterize attentiveness and inattentiveness due to the unique characteristic of each individual. Therefore, the aim of this research is to define the attentiveness state of each subject from large number of samples in controlled parameters to minimize the variability gap of the ERP peak between each individual. The experiment has been conducted using driving simulator to obtain the EEG data from two groups of subjects which were categorized as attentive and inattentive state by using two distinct stimulations i.e., listening to radio and no stimulation. The obtained results show significant boundary and similarity patterns for the level of attentiveness in both groups. Due to these patterns, a hybrid mean-fuzzy (HMF) technique was proposed to analyze the peak of N170 ERP decrement value versus the accident score based on the driving performance and attention threshold was determined accordingly. Three levels of attention namely ‘attentive’, ‘the beginning of inattentiveness’ and ‘inattentive’ state were presented within a new framework scale in the form of a fish bone diagram known as Attention Degradation Scale (ADS). In order to validate the feasibility of the proposed ADS for both groups, the analysis of the data has been done with and without ADS. Based on the outcome, 52% of the subjects were detected as attentive whilst 56% were in inattentive state which is significant as the percentage obtained with ADS was more than without it. Finally, a prototype application has been implemented to prove the theoretical data of attention level prediction. The prototype has successfully warned the subjects of potential accidents whenever the attention level was below the threshold value. Therefore, the findings of this research can be a promising foundation for alarm system which based on attention recognition technique that potentially would be able to reduce road accidents specifically with the proposed ADS. 2019 Thesis http://eprints.utem.edu.my/id/eprint/24512/ http://eprints.utem.edu.my/id/eprint/24512/1/Electrophysiological%20Degrading%20Correlates%20For%20Driving%20Attention%20Loss%20Threshold%20Determination.pdf text en public http://eprints.utem.edu.my/id/eprint/24512/2/Electrophysiological%20Degrading%20Correlates%20For%20Driving%20Attention%20Loss%20Threshold%20Determination.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=117159 phd doctoral Universiti Teknikal Malaysia Melaka Faculty of Electronic and Computer Engineering 1. Abrahantes, J.C., Serroyen, J., Geys, H., Molenberghs, G., and Drinkenburg, W.H.I.M., 2007. Statistical methods for EEG data. In: Isotopic and Molecular Processes. pp.65–129. 2. Adam, A. 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