Development of an IoT-Based Drowning Detection System for Private Swimming Pools

Anunuso Justice C, Mustapha Hafiz Bola, Chika Innocent, Aba Ojonimi King, Thomas Alhassan Mamman, Temple C. Okeahialam and Callistus Tochukwu Ikwuazom
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Anunuso Justice C: Department of Mechatronic Engineering, Federal University of Technology, Minna, 920101, Nigeria
Mustapha Hafiz Bola: Department of Software Engineering, Veritas University, Abuja, 901101, Nigeria
Chika Innocent: Department of Telecommunications Engineering, Federal University of Technology, Minna, 920101, Nigeria
Aba Ojonimi King: Department of Mechatronic Engineering, Federal University of Technology, Minna, 920101, Nigeria
Thomas Alhassan Mamman: Department of Telecommunications Engineering, Federal University of Technology, Minna, 920101, Nigeria
Temple C. Okeahialam: Department of Information Technology, Federal University of Technology, Minna, 920101, Nigeria
Callistus Tochukwu Ikwuazom: Department of Information Technology, Federal University of Technology, Minna, 920101, Nigeria

International Journal of Research and Scientific Innovation, 2024, vol. 11, issue 10, 176-189

Abstract: Drowning is among the top three leading causes of unintentional death worldwide, accounting for over 8% of all injury-related deaths. Annually, over 22,000 deaths occur due to drowning in adults, teenagers, and children. The highest rate is among children aged 0–15 years. Available solutions to this murder (drowning) involve traditional fencing of swimming pools, the use of wrist bands with RF connections, heartbeat sensors, monitoring systems using LASER and LDR, etc. These methods are not sufficient to solve the problems, as past methods involve wearable buildings systems. This paper proposes a method that is dependent on the swimming pool (the swimmer does not need to wear any device). The proposed system utilizes multiple sensor technology and IoT technology; PIR sensor for motion detection and Raspberry Pi module v3 for capturing and detecting possible drowning events via a trained model using yolo v5 architecture integrated into Raspberry Pi 4 8GB RAM. The system sends an email message to the authorized user if it senses motion around the swimming pool environment, detects a human in an un-save zone, and sends an alert of possible drowning.

Date: 2024
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