Driver drowsiness detection using Gray Wolf Optimizer based on voice recognition

Journal article

Sasim, S. S., Hassan, A. K. A. and Turner, S. 2022. Driver drowsiness detection using Gray Wolf Optimizer based on voice recognition. Aro - The Scientific Journal of Koya University. 10 (2), pp. 142-151. https://doi.org/10.14500/aro.11000

Publication dates
Authors	Sasim, S. S., Hassan, A. K. A. and Turner, S.
Abstract	Globally, drowsiness detection prevents accidents. Blood biochemicals, brain impulses, etc., can measure tiredness. However, due to user discomfort, these approaches are challenging to implement. This article describes a voice-based drowsiness detection system and shows how to detect driver fatigue before it hampers driving. A neural network and Gray Wolf Optimizer are used to classify sleepiness automatically. The recommended approach is evaluated in alert and sleep-deprived states on the driver tiredness detection voice real dataset. The approach used in speech recognition is mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs). The SVM algorithm has the lowest accuracy (71.8%) compared to the typical neural network. GWOANN employs 13-9-7-5 and 30-20-13-7 neurons in hidden layers, where the GWOANN technique had 86.96% and 90.05% accuracy, respectively, whereas the ANN model achieved 82.50% and 85.27% accuracy, respectivel
Keywords	Drowsiness ; Artificial neural network; Feature extraction; Gray Wolf Optimizer; Normalization; Mel-frequency cepstral coefficients; Linear prediction coefficients
Year	2022
Journal	Aro - The Scientific Journal of Koya University
Journal citation	10 (2), pp. 142-151
Publisher	Koya University
ISSN	2307-549X
Digital Object Identifier (DOI)	https://doi.org/10.14500/aro.11000
Official URL	https://aro.koyauniversity.org/index.php/aro/article/view/1000/298
Print	05 Dec 2022
Publication process dates
Accepted	Nov 2022
Deposited	12 Dec 2022
Publisher's version	1000-Article Text-4195-1-10-20221205.pdf License CC BY-NC-SA 4.0 File Access Level Open
Output status	Published
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