IOT based Drowsiness Detection System for Road Safety

Authors

  • Dr.M. Nithya Professor & Head, Department of Computer Science & Engineering, Vinayaka Mission’s Kirupananda Variyar Engineering College, Vinayaka Mission’s Research Foundation (Deemed To Be University) Salem Author
  • Narmadha T. Assistant Professor, Department of Computer Science & Engineering, Vinayaka Mission’s Kirupananda Variyar Engineering College, Vinayaka Mission’s Research Foundation (Deemed To Be University) Salem Author
  • Hariharan R. M.E. CSE, Department of Computer Science & Engineering, Vinayaka Mission’s Kirupananda Variyar Engineering College, Vinayaka Mission’s Research Foundation (Deemed To Be University) Salem Author

DOI:

https://doi.org/10.61841/be71qv70

Keywords:

Drowsiness Detection, Raspberry-Pi, Camera Module, IOT, EAR

Abstract

In the current situation, the vehicle utilization rate is expanding. Thus, the quantity of mishaps is additionally expanding. As per the look into it, most of the mishaps are because of the driver's recklessness. Every year, among them all, the number of deaths is 1.24 million because of vehicle mishaps. In India, the underlying drivers of these mishaps are the inebriated driver, drowsiness, and severely structured speed breakers. There is no viable system to forestall these main drivers. Our proposed framework gives a proficient, practical, and constant answer for forestalling vehicle mishaps. In this paper, we proposed a framework that is completely non-meddlesome and constant. Our proposed framework utilized the eye conclusion proportion as an information parameter to recognize the drowsiness of the driver. On the off chance that the eye conclusion proportion breaks down from the standard proportion, the driver is alerted with the assistance of a ringer. For our framework, a Pi camera is utilized to catch the pictures of the driver's eye, and the whole framework is consolidated utilizing Raspberry Pi. At long last SMS alert was sent to the proprietor's mobile. 

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References

[1] Anilkumar, C.V., Ahmed, M., Sahana, R., Thejashwini, R., & Anisha, P.S. (2016). Design of drowsiness, heartbeat detection system, and alertness indicator for driver safety. 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT).

[2] Devi, M.S., Choudhari, M.V., & Bajaj, P. (2011). Driver Drowsiness Detection Using Skin Color Algorithm and Circular Hough Transform. 2011 Fourth International Conference on Emerging Trends in Engineering & Technology.

[3] Katyal, Y., Alur, S., & Dwivedi, S. (2014). Safe driving by detecting lane discipline and driver drowsiness. 2014 IEEE International Conference on Advanced Communications, Control, and Computing Technologies.

[4] Kim, D., Han, H., Cho, S., & Chong, U. (2012). Detection of drowsiness with eyes open using EEG-based power spectrum analysis. 2012 7th International Forum on Strategic Technology (IFOST).

[5] Manu, B.N. (2016). Facial features monitoring for real-time drowsiness detection. 2016 12th International Conference on Innovations in Information Technology (IIT).

[6] Stanley, P.K., Jaya Prahash, T., Sibin Lal, S., & Daniel, P.V. (2017). Embedded based drowsiness detection

using EEG signals. 2017 IEEE International Conference on Power, Control, Signals and Instrumentation

Engineering (ICPCSI).

[7] Omidyeganeh, M., Javadtalab, A., & Shirmohammadi, S. (2011). Intelligent driver drowsiness detection through fusion of yawning and eye closure. 2011 IEEE International Conference on Virtual Environments, Human-Computer Interfaces, and Measurement Systems Proceedings.

[8] Tianyi Hong & Huabiao Qin. (2007). Driver drowsiness detection in an embedded system. 2007 IEEE International Conference on Vehicular Electronics and Safety.

[9] Tabrizi, P.R., & Zoroofi, R.A. (2009). Drowsiness Detection Based on Brightness and Numeral Features of Eye Image. 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

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Published

31.07.2020

How to Cite

M. , N., T. , N., & R. , H. (2020). IOT based Drowsiness Detection System for Road Safety. International Journal of Psychosocial Rehabilitation, 24(5), 7290-7296. https://doi.org/10.61841/be71qv70