Drowsiness detection and alert system using face recognition with Raspberry Pi / Nur Syazwina Mohd Fadzalisham, Nor Adora Endut and Muhammad Nizamuddin Rosli

Mohd Fadzalisham, Nur Syazwina and Endut, Nor Adora and Rosli, Muhammad Nizamuddin (2023) Drowsiness detection and alert system using face recognition with Raspberry Pi / Nur Syazwina Mohd Fadzalisham, Nor Adora Endut and Muhammad Nizamuddin Rosli. In: International Jasin Multimedia & Computer Science Invention and Innovation Exhibition (i-JaMCSIIX 2023). Faculty of Computer and Mathematical Sciences, Kampus Jasin, pp. 53-56. ISBN 978-967-15337-0-3 (Submitted)

Abstract

Recently, drowsiness detection has garnered significant attention due to its crucial implications in various industries, such as transportation, healthcare, and workplace safety. Drowsiness, often caused by exhaustion or lack of sleep, poses serious risks to people's safety and well-being. Even in less critical situations, like during online classes, feeling sleepy negatively impacts students' learning and academic performance. The occurrence of accidents and errors resulting from drowsiness has underscored the need for effective detection technologies to mitigate these risks. The main objective of this project is to develop a drowsiness detection and alert system using a Raspberry Pi. The technology aims to analyse facial features in real-time, efficiently identifying key markers of drowsiness through computer vision techniques and machine learning algorithms. Leveraging Raspberry Pi as the camera component offers a portable and cost-effective solution suitable for various settings. The solution integrates a Telegram bot for streamlined communication, utilizing Pi Camera to capture facial photos and promptly detect signs of drowsiness. This bot sends rapid alert messages to users' mobile phones or laptops, enabling swift responses to any concerns related to potential drowsiness, thereby enhancing safety and well-being. The system also proficiently records essential drowsiness data in a MySQL database, allowing for further analysis and insights to continuously improve and enhance effectiveness in reducing drowsiness-related incidents.

Metadata

Item Type: Book Section
Creators:
Creators
Email / ID Num.
Mohd Fadzalisham, Nur Syazwina
2020862416@student.uitm.edu.my
Endut, Nor Adora
noradora@uitm.edu.my
Rosli, Muhammad Nizamuddin
nizamuddin.rosli@gmail.com
Contributors:
Contribution
Name
Email / ID Num.
UNSPECIFIED
Md Badarudin, Ismadi
UNSPECIFIED
UNSPECIFIED
Jasmis, Jamaluddin
UNSPECIFIED
UNSPECIFIED
Jono, Mohd Hajar Hasrol
UNSPECIFIED
UNSPECIFIED
Suhaimi, Nur Suhailayani
UNSPECIFIED
UNSPECIFIED
Mat Zain, Nurul Hidayah
UNSPECIFIED
UNSPECIFIED
Abdullah Sani, Anis Shobirin
UNSPECIFIED
UNSPECIFIED
Halim, Faiqah Hafidzah
UNSPECIFIED
UNSPECIFIED
Abd Kadir, Siti Aisyah
UNSPECIFIED
UNSPECIFIED
Jalil, Ummu Mardhiah
UNSPECIFIED
Subjects: T Technology > T Technology (General) > Integer programming
Divisions: Universiti Teknologi MARA, Melaka > Jasin Campus > Faculty of Computer and Mathematical Sciences
Event Title: International Jasin Multimedia & Computer Science Invention and Innovation Exhibition (i-JaMCSIIX 2023)
Event Dates: 8th November 2023
Page Range: pp. 53-56
Keywords: Drowsiness detection; Raspberry Pi; Facial recognition; Machine learning; Workplace safety
Date: 2023
URI: https://ir.uitm.edu.my/id/eprint/94301
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