Affiliations: [a] Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| [b] School of Data Science, Nagoya City University, Nagoya, Japan
| [c] Virtual Reality Laboratory, K.N. Toosi University of Technology, Tehran, Iran
| [d] Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran
| [e] Department Occupational Sleep Research Center, Baharloo Hospital, Tehran University of Medical Sciences, Tehran, Iran
| [f] Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Correspondence:
[*]
Address for correspondence: Adel Mazloumi, Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, P.O. Box 6446, Tehran 14155, Iran. E-mail: amazlomi@tums.ac.ir.
Abstract: BACKGROUND:Numerous systems for detecting driver drowsiness have been developed; however, these systems have not yet been widely used in real-time. OBJECTIVE:The purpose of this study was to investigate at the feasibility of detecting alert and drowsy states in drivers using an integration of features from respiratory signals, vehicle lateral position, and reaction time and out-of-vehicle ways of data collection in order to improve the system’s performance and applicability in the real world. METHODS:Data was collected from 25 healthy volunteers in a driving simulator-based study. Their respiratory activity was recorded using a wearable belt and their reaction time and vehicle lateral position were measured using tests developed on the driving simulator. To induce drowsiness, a monotonous driving environment was used. Different time domain features have been extracted from respiratory signals and combined with the reaction time and lateral position of the vehicle for modeling. The observer of rating drowsiness (ORD) scale was used to label the driver’s actual states. The t-tests and Man-Whitney test was used to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features then combined to investigate the improvement in performance using the Multilayer Perceptron (MLP), the Support Vector Machines (SVMs), the Decision Trees (DTs), and the Long Short Term Memory (LSTM) classifiers. The models were implemented in Python library 3.6. RESULTS:The experimental results illustrate that the support vector machine classifier achieved accuracy of 88%, precision of 85%, recall of 83%, and F1 score of 84% using selected features. CONCLUSION:These results indicate the possibility of very accurate detection of driver drowsiness and a viable solution for a practical driver drowsiness system based on combined measurement using less-intrusive and out-of-vehicle recording methods.
Keywords: Sleepiness, respiration, classification, machine learning, reaction time, automobile driving
DOI: 10.3233/WOR-230179
Journal: Work, vol. 77, no. 4, pp. 1165-1177, 2024
