Drowsiness Detector System Development for Late Night Shift Workers Using Support Vector Machine Method with Neurosky Mindwave Mobile

by Izzat Aulia Akbar,Febriliyan Samopa,Bambang Setiawan,Hatma Suryotrisongko,Faizal Johan Atletiko,Radityo P Wibowo
( Institut Teknologi Sepuluh Nopember, Institut Teknologi Sepuluh Nopember, Institut Teknologi Sepuluh Nopember, Institut Teknologi Sepuluh Nopember, Institut Teknologi Sepuluh Nopember, Institut Teknologi Sepuluh Nopember )

Date Published: 02 Dec 2013
Published In: Information Systems International Conference (ISICO)
Volume: 2013
Publisher: Departemen Sistem Informasi, Institut Teknologi Sepuluh Nopember
Language: id-ID

Keywords: SVM,EEG,Drowsiness,Driving Simulator,Late Night Shift Workers

Abstract

Drowsiness is the common thing that happens to every human. However, this could cause some problems. There have been many accidents happen in Indonesia, that worker must lose the part of his/her body, disability, and even life because of the drowsiness factor while doing work. In addition to solve this matter, an application is needed to give warning for workers when it detects the drowsiness. By using the brainwave receiver sensor, the application can give early warning in real-time based on worker condition. This research involves 5 subjects were using Neurosky Mindwave Mobile for 3 hours from 21.00 to 24.00. Most people will begin to feel sleepy during this period of time. Each subject did twice experiments. The data gathered would be used to perform SVM training and testing. The data will be validated with β/α and (α+θ)/β signal index. This research uses SVM as a mean to detect the level of drowsiness in brain waves. The result of this research is the drowsiness detection application with accuracy for approximately 87.40% according to the results of the testing application of the 3 subjects who performed the valid training data.


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