IOT—Eye Drowsiness Detection System by Using Intel Edison with GPS Navigation
The number of traffic accidents continues to increase due to the driver’s fatigue has become a serious problem to the society especially for the driver who drove for long distance. Technology in digital computer system allows us to create a drowsiness detection system. Studies for drowsiness detector system have focused on development of computer vision algorithm and lack of Internet of Things (IoT) and notification system, either awake or sleep or might involve in accident, and current location. Thus, we decide to develop a drowsiness detection system with notification of accident and the location by using Global Positioning System (GPS) navigation. In this system, if the driver’s eyes are closed about more than 4 s, the driver considers as drowsy and an alarm system will be activated to warn the driver and notify the status and location to relative for further action via message (SMS).
KeywordsEye drowsiness Intel edison GPS navigation IoT Smartphone setup
We would like to acknowledge funding provided by Universiti Malaysia Pahang (RDU1703233).
- 1.Ruxyn, T.: Says.com. Retrieved from http://says.com/my/news/malaysia-sroads-among-the-world-s-most-dangerous-and-deadliest. Last accessed 01 Sept 2018
- 2.https://www.hmetro.com.my/mutakhir/2018/03/324770/derita-kereta-terhumban-dalam-gaung. Last accessed 01 Sept 2018
- 3.http://www.sinarharian.com.my/mobile/semasa/dua-kanak-kanak-maut-dalam-kemalangan-di-lpt-1.406312. Last Accessed 01 Sept 2018
- 7.Das, P., Pragadeesh, S.: A microcontroller based car-safety system: implementing drowsiness detection and vehicle-vehicle distance detection in parallel. Int. J. Sci. Technol. Res. 4(2) (2015)Google Scholar
- 8.Kulkarni, A.S., Shinde, S.B.: A review paper on monitoring driver distraction in real time using computer vision system. Int. J. Comput. Sci. Eng. 5(6) (2017)Google Scholar
- 9.Kulkarni, S.S., Harale, A.D., Thakur, A.V.: Image processing for driver’s safety and vehicle control using raspberry Pi and webcam. In: 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pp. 1288–1291 (2017)Google Scholar
- 10.You, C.-W., et al.: CarSafe: a driver safety app that detects dangerous driving behavior using dual-cameras on smartphones. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM (2012)Google Scholar
- 11.https://github.com/tahaemara/sleep-detection. Last accessed 01 Sept 2018
- 12.Ousler 3rd, G.W., Abelson, M.B., Johnston, P.R., Rodriguez, J., Lane, K., Smith, L.M.: Blink patterns and lid-contact times in dry-eye and normal subjects. Clin. Ophthalmol. (Auckland, NZ) 8, 869 (2014)Google Scholar