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
AuthorsSasim, 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

KeywordsDrowsiness ; Artificial neural network; Feature extraction; Gray Wolf Optimizer; Normalization; Mel-frequency cepstral coefficients; Linear prediction coefficients
Year2022
JournalAro - The Scientific Journal of Koya University
Journal citation10 (2), pp. 142-151
PublisherKoya University
ISSN2307-549X
Digital Object Identifier (DOI)https://doi.org/10.14500/aro.11000
Official URLhttps://aro.koyauniversity.org/index.php/aro/article/view/1000/298
Publication dates
Print05 Dec 2022
Publication process dates
AcceptedNov 2022
Deposited12 Dec 2022
Publisher's version
License
File Access Level
Open
Output statusPublished
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