Anti Sleep Alarm System

Abstract

In modern-times, owing to hectic schedules it becomes very difficult to remain active all the time. Imagine a situation where a person is driving home from work, dead tired after facing all the challenges of the day. The hands are on the wheel and foot on the pedal but suddenly started feeling drowsy, the eyes start shutting and the vision blurs and before it knew, then the person fall asleep. Falling asleep on the wheel can lead to serious consequences, there may be accidents and people may even lose their lives. This situation is much more common and hence, it is very important to counter this problem. So to address this issue, the Project Anti Sleep Alarm for Drivers is introduced. This system alerts the Person falls asleep at the wheel thereby, avoiding accidents and saving lives. This system is useful especially for people who travel long distances and people who are driving late at night. The circuit is built using Arduino Nano, a switch, a Piezo buzzer, Micro Vibration Motor and an Eye blink sensor. Whenever the driver feels sleepy and asleep the eye blink sensor detects and the buzzer turn ON with a sound of an intermediate beep. When driver comes back to his normal State eye blink sensor senses that and buzzer turns OFF.

Introduction

I. INTRODUCTION

The drowsiness detection system is capable of detecting drowsiness in quickly. The system which can differentiate normal eye blink and drowsiness can prevent the driver from entering the state of sleepiness while driving. The system works well irrespective of driver wearing spectacles and under low light conditions also.

During the monitoring, the system is able to decide if the eyes are closed or opened. When the eyes have been closed for too long a warning signal is issued.

The ultimate goal of the system is to check the drowsiness condition of the driver. Based on the eye movements of the driver, the drowsiness is detected and according eye blink, the alarm will be generated to alert the driver and to reduce the speed of the vehicle along with the indication of parking light. By doing this, many accidents will be reduced and provides safety to the driver and vehicle.

A system that is driver safety and car security is presented only in luxurious costly cars. Using eye detection, driver security and safety can be implemented in normal car also.  

II. LITERATURE REVIEW

At first, Dreißig, et al. [2] designed and tested a feature selection technique founded on the kNearest Neighbor algorithm for the driver's state classification.

Their result of the research showed that the impact of sleepiness on the driver's blink behavior and head motions is shown in a final analysis of the highest performing feature sets.

Another work Baronti, et al. [4] described a minimal, basic distributed force sensor that is ideal for sensing handgrip strength and hand location on a wheel.

They suggested that the sensor can be utilized in active safety systems in automobiles to detect driver drowsiness.Chieh, et al. [3] offered a technique for detecting drowsiness in automobiles by measuring the driver's grip force on the steering wheel and comparing it to the fluctuation in grip force caused by weariness or loss of attentiveness. Vural, et al. [5] described a video-based method for detecting driver sleepiness.

Throughout sleepiness periods, they used machine learning to analyze authentic human behavior. Kurt, et al. [6] created a new approach for automatically estimating vigilance level Utilizing electroencephalogram (EEG), electromyogram (EMG), and eye movement (EOG) data captured during the changeover from awake to sleep. They established this approach for concentration requiring tasks like lorry driver. Khushaba, et al.

Conclusion

This Project “ANTI-SLEEP ALARM FOR DRIVERS” is successfully designed, and tested and demo unit is fabricated. The goal of this project is to develop a device that can accurately detect sleepy driving and make alarms accordingly, which aims to prevent the drivers from drowsy driving and create a safer driving environment. The project was accomplished by an IR sensor. This system detects the drowsiness in quickly. This system which can differentiate normal eye blink and drowsiness can prevent the driver from entering the state of sleepiness while driving. Whenever a driver asleep due to drowsiness, the buzzer continuously starts beeping unless the driver gets back to his/her normal position. The ultimate goal of the system is to prevent the road accident, where the values measured in life.

References

[1] L.Webb & Taylor. (2020, Nov 26). Drivers are Falling Asleep Behind the Wheel. Available: https://www.webbfirmattorneys.com/blog/2020/11/drivers-are-falling-asleep-behind-thewhee [2] M. Dreißig, M. H. Baccour, T. Schäck, and E. Kasneci, \"Driver Drowsiness Classification Based on Eye Blink and Head Movement Features Using the k-NN Algorithm,\" in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020, pp. 889-896: IEEE. [3] T. C. Chieh, M. M. Mustafa, A. Hussain, E. Zahedi, and B. Y. Majlis, \"Driver fatigue detection using steering grip force,\" in Proceedings. Student Conference on Research and Development, 2003. SCORED 2003., 2003, pp . 45-48: IEEE. [4] F. Baronti, F. Lenzi, R. Roncella, and R. Saletti, \"Distributed sensor for steering wheel grip force measurement in driver fatigue detection,\" in 2009 Design, Automation & Test in Europe Conference & Exhibition, 2009, pp. 894-897: IEEE. [5] E. Vural, M. Çetin, A. Erçil, G. Littlewort, M. Bartlett, and J. Movellan, \"Machine learning systems for detecting driver drowsiness,\" in In-vehicle corpus and signal processing for driver behavior: Springer, 2009, pp. 97-110. [6] M. B. Kurt, N. Sezgin, M. Akin, G. Kirbas, and M. J. E. S. w. A. Bayram, \"The ANN-based computing of drowsy level,\" vol. 36, no. 2, pp. 2534-2542, 2009. [7] R. N. Khushaba, S. Kodagoda, S. Lal, and G. J. I. t. o. b. e. Dissanayake, \"Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm,\" vol. 58, no. 1, pp. 121-131, 2010. [8] B. Alshaqaqi, A. S. Baquhaizel, M. E. A. Ouis, M. Boumehed, A. Ouamri, and M. Keche, \"Driver drowsiness detection system,\" in 2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA), 2013, pp. 151-155: IEEE. [9] A. Nano, \"Arduino Nano,\" ed: Obtenido de Arduino: https://store. arduino. cc/usa/arduinonano, 2018.

Copyright

Copyright © 2024 Prajwal Patil, Atharva Pawase, Mahesh Patil, Shweta Padwal , Yash Pawar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.