IDEAS home Printed from https://ideas.repec.org/a/bjb/journl/v14y2025i4p270-276.html
   My bibliography  Save this article

Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver using Machine Learning Approach

Author

Listed:
  • D Naresh Kumar

    (PG Student, Department of Computer Application –PG VISTAS, Chennai)

  • H. Jayamangala

    (Assistant Professor, Department of Computer Application –PG VISTAS, Chennai)

Abstract

Drowsiness is among the primary reasons for driver caused traffic accidents. The interactive systems that have been designed to minimize road accidents by notifying the drivers are referred to as Advanced Driver Assistance Systems (ADAS). Most significant ADAS include Lane Departure Warning System, Front Collision Warning System and Driver Drowsiness Systems. In the current research, an eye state detection based ADAS system is introduced to identify driver drowsiness. To start, Viola-Jones algorithm method is utilized for identifying the face and eye regions in the current work. The eye region, detected in the present method, is classified into open or closed through utilization of a machine learning approach. Ultimately, eye conditions are inspected at time domain using percentage of eyelid closure (PERCLOS) metric and drowsiness states are calculated by Support Vector Machine (SVM). The above proposed methods are tested on 7 real individuals and drowsiness conditions are detected better accuracy, respectively.

Suggested Citation

  • D Naresh Kumar & H. Jayamangala, 2025. "Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver using Machine Learning Approach," International Journal of Latest Technology in Engineering, Management & Applied Science, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 14(4), pages 270-276, April.
  • Handle: RePEc:bjb:journl:v:14:y:2025:i:4:p:270-276
    as

    Download full text from publisher

    File URL: https://www.ijltemas.in/DigitalLibrary/Vol.14Issue4/270-276.pdf
    Download Restriction: no

    File URL: https://www.ijltemas.in/papers/volume-14-issue-4/270-276.html
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjb:journl:v:14:y:2025:i:4:p:270-276. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Pawan Verma (email available below). General contact details of provider: https://www.ijltemas.in/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.