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Developing a System for High-resolut...
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Hassan, Ahnaf Rashik.
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Developing a System for High-resolution Detection of Driver Drowsiness Using Physiological Signals.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Developing a System for High-resolution Detection of Driver Drowsiness Using Physiological Signals./
作者:
Hassan, Ahnaf Rashik.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:
95 p.
附註:
Source: Masters Abstracts International, Volume: 80-05.
Contained By:
Masters Abstracts International80-05.
標題:
Neurosciences. -
電子資源:
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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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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