Gazing Time Analysis for Drowsiness Assessment Using Eye Gaze Tracker

Rumagit, Arthur M.; Akbar, Izzat Aulia; Igasaki, Tomohiko

doi:10.12928/telkomnika.v15i1.6145

Cited by 5 publications

(1 citation statement)

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“…Detecting the drowsy state in a driving environment has mostly been done through camera-based vision technology using human face variability. This vision technology has achieved sufficient detection accuracy [31,32]. However, if the subjects wear glasses or do not look straight ahead, the camera cannot detect drowsy state robustly [33].…”

Section: Introductionmentioning

confidence: 99%

Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals

Jeong

Lee

et al. 2019

Brain Sciences

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Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot’s mental state is a critical issue as more than 70% of aviation accidents are caused by human factors, such as fatigue or drowsiness. In this study, we report the classification of not only two mental states (i.e., alert and drowsy states) but also five drowsiness levels from electroencephalogram (EEG) signals. To the best of our knowledge, this approach is the first to classify drowsiness levels in detail using only EEG signals. We acquired EEG data from ten pilots in a simulated night flight environment. For accurate detection, we proposed a deep spatio-temporal convolutional bidirectional long short-term memory network (DSTCLN) model. We evaluated the classification performance using Karolinska sleepiness scale (KSS) values for two mental states and five drowsiness levels. The grand-averaged classification accuracies were 0.87 (±0.01) and 0.69 (±0.02), respectively. Hence, we demonstrated the feasibility of classifying five drowsiness levels with high accuracy using deep learning.

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Section: Introductionmentioning

confidence: 99%