Year

2018

Season

Spring

Paper Type

Master's Thesis

College

College of Computing, Engineering & Construction

Degree Name

Master of Science in Computer and Information Sciences (MS)

Department

Computing

NACO controlled Corporate Body

University of North Florida. School of Computing

First Advisor

Dr. Karthikeyan Umapathy

Second Advisor

Dr. Ching-Hua Chuan

Third Advisor

Dr. Debbie Wang

Department Chair

Dr. Sherif Elfayoumy

College Dean

Dr. Mark A. Tumeo

Abstract

Driver fatigue is a state of reduced mental alertness which impairs the performance of a range of cognitive and psychomotor tasks, including driving. According to the National Highway Traffic Safety Administration, driver fatigue was responsible for 72,000 accidents that lead to more than 800 deaths in 2015. A reliable method of driver fatigue detection is needed to prevent such accidents. There has been a great deal of research into studying driver fatigue via electroencephalography (EEG) to analyze brain wave data. These research works have produced three competing EEG data-based ratios that have the potential to detect driver fatigue.

Research has shown these three ratios trend downward as fatigue increases. However, no empirical research has been conducted to determine whether drivers begin to feel fatigue at a certain Percent Change from an alert state to a fatigue state in one or more of these ratios. If a Percent Change could be identified for which drivers begin to feel fatigue, then it could be used as a method of fatigue detection in real-time system. This research focuses on answering this question by collecting brain wave data via an EEG device over a 60-minute driving session for 10 University of North Florida (UNF) students. A frequency distribution and cluster analysis was done to identify a common Percent Change for the participants who experienced fatigue. The results of the analysis were compared to a subset of users who did not experience fatigue to validate the findings. The project was approved by the UNF IRB on Nov. 1, 2016 (reference number 475514-4).

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