Version 1
: Received: 12 October 2024 / Approved: 14 October 2024 / Online: 14 October 2024 (13:25:50 CEST)
How to cite:
Shatnawi, M.; Albreiki, F. A.; Alkhoori, A.; Alhebshi, M.; Shatnawi, A. Advances and Challenges in Automated Drowning Detection and Prevention Systems. Preprints2024, 2024101058. https://doi.org/10.20944/preprints202410.1058.v1
Shatnawi, M.; Albreiki, F. A.; Alkhoori, A.; Alhebshi, M.; Shatnawi, A. Advances and Challenges in Automated Drowning Detection and Prevention Systems. Preprints 2024, 2024101058. https://doi.org/10.20944/preprints202410.1058.v1
Shatnawi, M.; Albreiki, F. A.; Alkhoori, A.; Alhebshi, M.; Shatnawi, A. Advances and Challenges in Automated Drowning Detection and Prevention Systems. Preprints2024, 2024101058. https://doi.org/10.20944/preprints202410.1058.v1
APA Style
Shatnawi, M., Albreiki, F. A., Alkhoori, A., Alhebshi, M., & Shatnawi, A. (2024). Advances and Challenges in Automated Drowning Detection and Prevention Systems. Preprints. https://doi.org/10.20944/preprints202410.1058.v1
Chicago/Turabian Style
Shatnawi, M., Mariam Alhebshi and Anas Shatnawi. 2024 "Advances and Challenges in Automated Drowning Detection and Prevention Systems" Preprints. https://doi.org/10.20944/preprints202410.1058.v1
Abstract
Drowning is among the most common reasons of children’s death aged one to fourteen around the globe. With rising populations and the growing popularity of swimming pools in hotels and villas, the incidence of drowning has accelerated. Accordingly, the development of systems for detecting and preventing drowning has become increasingly critical to provide safe swimming settings. In this paper, we propose a comprehensive review of recent existing advancements in automated drowning detection and prevention systems. The existing approaches can be broadly categorized according to their objectives into two main groups: detection-based systems and detection and rescue-based systems. Automatic drowning detection approaches could be further categorized into computer vision-based approaches where camera-captured images are analyzed by machine learning algorithms to detect instances of drowning, and sensing-based approaches where sensing instruments are attached to swimmers to track their behavior. We explore the advantages and limitations of each approach. Additionally, we discuss the technical challenges and unresolved issues related to this domain. Based on this study, we provide researchers and practitioners with a comprehensive analysis of the state-of-the-art techniques for drowning detection and prevention.
Keywords
Drowning detection; prevention; rescue; Swimming pool safety; IoT; artificial intelligence: AI; deep learning; machine learning; surveillance technology
Subject
Engineering, Control and Systems Engineering
Copyright:
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.
