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Developing a System for High-resolut...

Hassan, Ahnaf Rashik.

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Developing a System for High-resolution Detection of Driver Drowsiness Using Physiological Signals.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Developing a System for High-resolution Detection of Driver Drowsiness Using Physiological Signals./
Author:	Hassan, Ahnaf Rashik.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	95 p.
Notes:	Source: Masters Abstracts International, Volume: 80-05.
Contained By:	Masters Abstracts International80-05.
Subject:	Neurosciences. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10937253
ISBN:	9780438672581

Developing a System for High-resolution Detection of Driver Drowsiness Using Physiological Signals.
Hassan, Ahnaf Rashik.

Developing a System for High-resolution Detection of Driver Drowsiness Using Physiological Signals. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 95 p.

Source: Masters Abstracts International, Volume: 80-05.

Thesis (M.A.S.)--University of Toronto (Canada), 2018.

This item must not be sold to any third party vendors.

Background: This research aims to develop a high-resolution, reliable, and efficient drowsiness detection system. Existing systems for detecting drowsiness are of low-resolution, expensive, dependent on external parameters, or are inconvenient for the driver. Method: Two studies were conducted: First, we analyzed electroencephalogram (EEG) data collected during a sleep study to develop a high-resolution drowsiness detection algorithm. This algorithm was then tested in a second study that actively engaged participants in a reaction time task. Results: In the sleep study, a sigmoid wake probability model yielded high drowsiness detection rates. In the reaction time study, however, the same method showed low sensitivity. Instead, a time-domain feature based algorithm performed best with high accuracy, high sensitivity, and high specificity. Significance: Upon successful validation of the developed algorithm in a driving study, this research will help to develop a reliable, wearable, and convenient device to detect drowsy driving that could increase road safety.

ISBN: 9780438672581Subjects--Topical Terms:

588700
Neurosciences.

Developing a System for High-resolution Detection of Driver Drowsiness Using Physiological Signals.
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300 $a 95 p.
500 $a Source: Masters Abstracts International, Volume: 80-05.
500 $a Publisher info.: Dissertation/Thesis.
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502 $a Thesis (M.A.S.)--University of Toronto (Canada), 2018.
506 $a This item must not be sold to any third party vendors.
520 $a Background: This research aims to develop a high-resolution, reliable, and efficient drowsiness detection system. Existing systems for detecting drowsiness are of low-resolution, expensive, dependent on external parameters, or are inconvenient for the driver. Method: Two studies were conducted: First, we analyzed electroencephalogram (EEG) data collected during a sleep study to develop a high-resolution drowsiness detection algorithm. This algorithm was then tested in a second study that actively engaged participants in a reaction time task. Results: In the sleep study, a sigmoid wake probability model yielded high drowsiness detection rates. In the reaction time study, however, the same method showed low sensitivity. Instead, a time-domain feature based algorithm performed best with high accuracy, high sensitivity, and high specificity. Significance: Upon successful validation of the developed algorithm in a driving study, this research will help to develop a reliable, wearable, and convenient device to detect drowsy driving that could increase road safety.
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