An Investigation on Driver’s Fatigue Using Eye Tracker in Addition to Electromyography and Possible Implementable Driver’s Fatigue Detection and Warning System Using Fuzzy Logic

The use of electromyography (EMG) to detect muscle region signals is a very nascent field that started rapid development due to technological advancement over the past decades. EMG is a diagnostic process that checks the health state of muscles and nerve cells. This involves having electrodes attached to each muscle region of interest to catch the transmitted electrical signals that cause muscles to contract and relax. These signals are then translated into graphs and numbers, which help health officials make proper diagnostics. The function this machine can carry out makes it possible to detect fatigue in muscles. The eye-tracking device is another technology that has made it possible to evaluate fatigue by monitoring changes in the drivers\u27 eye movements. Fatigue in drivers is one of the significant causes of road accidents. According to NHTSA, 72000 police-reported crashes between the years of 2013 and 2019 involved fatigued drivers. There is no standard test to determine fatigue as there is for intoxication, i.e., a “Breathalyzer.” State reporting practices are inconsistent. There is little or no police training in identifying drowsiness as a crash factor. Every state currently addresses fatigue in some way in their crash report forms, and self-reporting is unreliable. This thesis focuses on investigating drivers fatigue symptoms to test whether there are significant differences before and after drivers experience fatigue with the use of electromyography, eye tracking device, and a driving simulator and going ahead to develop a detection and warning system by simulation using fuzzy logic that is capable of identifying fatigue symptoms and executing countermeasures when these symptoms arise in drivers. In this investigation, five-leg muscle areas are observed under the EMG sensors to indicate the changes in EMG signals when a driver is tired and as well as to determine the best muscle region out of all five areas that generate the most significant results with regression analysis. In this study, the investigation will take considerable focus on the indicators like a change in EMG signal amplitude, changes in the central frequency of the EMG Signal, and all significant changes in the eye tracker like the pupil diameter, number of fixation, and saccades, to set up a rule-based system using fuzzy logic where a threshold will be pre-defined such that if the drivers are fatigued a countermeasure will be triggered to alert the driver