Automatic Driver Drowsiness Detection Using Artificial Neural Network Based on Visual Facial Descriptors: Pilot Study

Abstract

Papangkorn Inkeaw,1 Pimwarat Srikummoon,2,3 Jeerayut Chaijaruwanich,1,4 Patrinee Traisathit,2,3,5 Suphakit Awiphan,1,4 Juthamas Inchai,6 Ratirat Worasuthaneewan,7 Theerakorn Theerakittikul6,7 1Data Science Research Center, Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand; 2Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand; 3Data Science Research Center, Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand; 4Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand; 5Research Center in Bioresources for Agriculture, Industry and Medicine, Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand; 6Division of Pulmonary, Critical Care and Allergy, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand; 7Sleep Disorder Center, Center for Medical Excellence, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, ThailandCorrespondence: Theerakorn Theerakittikul, Tel [phone], Email [email protected]: Driving while drowsy is a major cause of traffic accidents globally. Recent technologies for detection and alarm within automobiles for this condition are limited by their reliability, practicality, cost, and lack of clinical validation. In this study, we developed an early drowsiness detection algorithm and device based on the “gold standard brain biophysiological signal” and facial expression digital data.Methods: The data were obtained from 10 participants. Artificial neural networks (ANN) were adopted as the model. Composite features of facial descriptors (ie, eye aspect ratio (EAR), mouth aspect ratio (MAR), face length (FL), and face width balance (FWB)) extracted from two-second video frames were investigated.Results: The ANN combined with the EAR and MAR features had the most sensitivity (70.12%) while the ANN combined with the EAR, MAR, and FL features had the most accuracy and specificity (60.76% and 58.71%, respectively). In addition, by applying the discrete Fourier transform (DFT) to the composite features, the ANN combined with the EAR and MAR features again had the highest sensitivity (72.25%), while the ANN combined with the EAR, MAR, and FL features had the highest accuracy and specificity (60.40% and 54.10%, respectively).Conclusion: The ANN with DFT combined with the EAR, MAR, and FL offered the best performance. Our direct driver sleepiness detection system developed from the integration of biophysiological information and internal validation provides a valuable algorithm, specifically toward alertness level.Keywords: drowsy driving, driver sleepiness detection

Keywords

• drowsy driving
• driver sleepiness detection