Monitoring the Variation in Driver Respiration Rate from Wakefulness to Drowsiness: A Non-Intrusive Method for Drowsiness Detection Using Thermal Imaging
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Abstract
Background and Objective: Driver drowsiness is a cause of many traffic accidents all around the world. Driver respiration dynamics undergoes significant changes from wakefulness to sleep. The intrusive nature of current respiration monitoring methods makes them unattractive for detection of in-vehicle driver drowsiness. In this paper, changes in the respiration rate were monitored for drowsiness detection using thermal imaging, which is completely contact-free and non-intrusive.
Materials and Methods: For each frame, the driver’s face was isolated from the rest of the image. Then, the nostrils' zone was localized using physiological characteristics of face. The respiration signal was constructed by putting together the mean temperature of nostril region in all of the frames. In order to study respiration variations from wakefulness to drowsiness, a total number of 12 subjects were tested in a driving simulator. The observer rating of drowsiness (ORD) method was used to estimate the drowsiness level of the subjects.
Results: Derivation of driver respiration rate using thermal imaging was a reliable and non-intrusive method. The results were strongly correlated with those of traditional methods. Driver respiration rate decreased from wakefulness to extreme drowsiness by 3.5 breaths per minute (bpm) and its standard deviation (SD) increased by 0.7 bpm.
Conclusion: Driver respiration rate decreased and its SD increased from wakefulness to drowsiness in 12 participants of this study. The results showed that driver drowsiness could be detected even at moderate levels from analysis of the respiration rate.
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Sigari MH, Fathy M, Soryani M. A driver face monitoring system for fatigue and distraction detection. International Journal of Vehicular Technology. 2013; 1-11.
Motlagh SJ, Shabany M, Haghighi KS, Nasrabadi AN, Razavi SH. Relationship between sleep quality, obstructive sleep apnea and sleepiness during day with related factors in professional drivers. Acta Medica Iranica. 2017; 55(11):690-695.
Dinges DF. An overview of sleepiness and accidents. Journal of Sleep Research. 1995; 4(s2):4-14.
Stoohs RA, Guilleminault C, Dement WC. Sleep apnea and hypertension in commercial truck drivers. Sleep. 1993; 16(8):11-4.
Trinder JO, Whitworth FE, Kay AM, Wilkin PA. Respiratory instability during sleep onset. Journal of Applied Physiology. 1992; 73(6):2462-9.
Douglas NJ, White DP, Pickett CK, Weil JV, Zwillich CW. Respiration during sleep in normal man. Thorax. 1982; 37(11):840-4.
Rodríguez-Ibáñez N, García González MÁ, Fernández Chimeno M, De Rosario H, Ramos Castro JJ. Synchrosqueezing index for detecting drowsiness based on the respiratory effort signal. In Proceedings of the XIII Mediterranean Conference on Medical and Biological Engineering and Computing. 2013; p. 965-8.
Gade R, Moeslund TB. Thermal cameras and applications: a survey. Machine Vision and Applications. 2014; 25(1):245-62.
Gustavsson R. Termography: A Practical Approach. Norbo kraftteknik AB; 2009.
Ring EF, Ammer K. Infrared thermal imaging in medicine. Physiological Measurement. 2012; 33(3):33-46.
Pereira CB, Yu X, Czaplik M, Rossaint R, Blazek V, Leonhardt S. Remote monitoring of breathing dynamics using infrared thermography. Biomedical Optics Express. 2015; 6(11):4378-94.
Bakhoda H. Analysis and implementation of a new driver drowsiness detection system based on thermal infrared imaging of the face. Master’s thesis. K. N. Toosi University of Technology. 2015.
Zhu Z, Fei J, Pavlidis I. Tracking Human Breath in Infrared Imaging. Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering. 2005; p. 227-31.
Pereira CB, Yu X, Blazek V, Leonhardt S. Robust remote monitoring of breathing function by using infrared thermography. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2015; p. 4250-3.
Daubechies I, Lu J, Wu HT. Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis. 2011; 30(2):243-61.
Wierwille WW, Ellsworth LA. Evaluation of driver drowsiness by trained raters. Accident Analysis & Prevention. 1994; 26(5):571-81.
Kotsiantis SB, Zaharakis I, Pintelas P. Supervised machine learning: A review of classification techniques. Informatica. 2007; 33:249-68.
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| Issue | Vol 3 No 1-2 (2018): Winter-Spring | |
| Section | Original Article(s) | |
| Keywords | ||
| Sleep Respiration Automobile driving Thermography | ||
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