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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Tomohiko Igasaki
Kumamoto University
Computer Science & IT

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Gazing Time Analysis for Drowsiness Assessment Using Eye Gaze Tracker Arthur Mourits Rumagit; Izzat Aulia Akbar; Tomohiko Igasaki
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 2: June 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i2.6145

Abstract

From several investigations, it has been shown that most of the traffic accidents were due to drowsy driving. In order to address this issue, many related works have been conducted. One study was able to capture the driver’s facial expression and estimate their drowsiness. Instead of measuring the driver’s physiological condition, the results of such measurements were also used to predict their drowsiness level in this study. We investigated the relationship between the drowsiness and physiological condition by employing an eye gaze signal utilizing an eye gaze tracker and the Japanese version of the Karolinska sleepiness scale (KSS-J) within the driving simulator environment. The results showed that the gazing time has a significant statistical difference in relation to the drowsiness level: alert (1−5), weak drowsiness (6−7), and strong drowsiness (8−9), with P<0.001. Therefore, we suggested the potential of using the eye gaze to assess the drowsiness under a driving condition. 
Gazing as actual parameter for drowsiness assessment in driving simulators Arthur Mourits Rumagit; Izzat Aulia Akbar; Mitaku Utsunomiya; Takamasa Morie; Tomohiko Igasaki
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i1.pp170-178

Abstract

Many traffic accidents are due to drowsy driving. However, to date, only a few studies have been conducted on the gazing properties related to drowsiness. This study was conducted with the objective of estimating the relationship between gazing properties and drowsiness in three facial expression evaluation (FEE) categories: alert (FEE = 0), lightly drowsy (FEE = 1−2), heavily drowsy (FEE = 3−4). Drowsiness was investigated based on these eye-gazing properties by analyzing the gazing signal utilizing an eye gaze tracker and FEE in a driving simulator environment. The results obtained indicate that gazing properties have significant differences among the three drowsiness conditions, with p < 0.001 in a Kruskal–Wallis test. Furthermore, the overall classification accuracy of the three drowsiness conditions based on gazing properties using a support vector machine was 76.3%. This indicates that our proposed gazing properties can be used to quantitatively assess drowsiness.
Measuring Cardiorespiratory Information in Sitting Position using Multiple Piezoelectric Sensors Tomohiko Igasaki; Makiko Kobayashi; Makiko Kobayashi
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 1: April 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i1.pp132-138

Abstract

We have been studying equipment to easily acquire cardiorespiratory information at home using piezoelectric sensors arranged on the seat surface of a chair. In our previous study, we suggested that the cardiac and respiratory components could be extracted by executing template matching using a two-dimensional cross-correlation function for the signals that were obtained from the piezoelectric sensors. However, there was a difficulty with the signal extraction, depending on the seating position. Therefore, in this study, we examined the measurement of the heartbeat and breathing interval using independent component analysis and multiple piezoelectric sensors. Moreover, the heartbeat and breathing intervals that were obtained from the extracted cardiorespiratory components using our developed automatic decision method were compared with those obtained from electrocardiogram and pneumogram. As a result, it was found that we could achieve better error rates (0.93±0.44% and 5.23±3.04% for the heartbeat and respiratory intervals, respectively) than in our previous study.
Co-Authors Arthur Mourits Rumagit Izzat Aulia Akbar Makiko Kobayashi Makiko Kobayashi Mitaku Utsunomiya Takamasa Morie