Advances and Challenges in Automated Drowning Detection and Prevention Systems
Drowning is among the most common reasons for children’s death aged one to fourteen around the globe, ranking as the third leading cause of unintentional injury death. With rising populations and the growing popularity of swimming pools in hotels and villas, the incidence of drowning has accelerated...
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MDPI AG
2024-11-01
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| author | Maad Shatnawi Frdoos Albreiki Ashwaq Alkhoori Mariam Alhebshi Anas Shatnawi |
| author_facet | Maad Shatnawi Frdoos Albreiki Ashwaq Alkhoori Mariam Alhebshi Anas Shatnawi |
| author_sort | Maad Shatnawi |
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| description | Drowning is among the most common reasons for children’s death aged one to fourteen around the globe, ranking as the third leading cause of unintentional injury death. 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, which alert lifeguards or parents to perform manual rescues, and detection and rescue-based systems, which integrate detection with automatic rescue mechanisms. 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 monitor their physical parameters. We explore the advantages and limitations of each approach. Additionally, we highlight technical challenges and unresolved issues related to this domain, such as data imbalance, accuracy, privacy concerns, and integration with rescue systems. We also identify future research opportunities, emphasizing the need for more advanced AI models, uniform datasets, and better integration of detection with autonomous rescue mechanisms. This study aims to provide a critical resource for researchers and practitioners, facilitating the development of more effective systems to enhance water safety and minimize drowning incidents. |
| format | Article |
| id | doaj-art-9e176a950bac4fea839054be2433a011 |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-9e176a950bac4fea839054be2433a0112024-11-26T18:06:42ZengMDPI AGInformation2078-24892024-11-01151172110.3390/info15110721Advances and Challenges in Automated Drowning Detection and Prevention SystemsMaad Shatnawi0Frdoos Albreiki1Ashwaq Alkhoori2Mariam Alhebshi3Anas Shatnawi4Department of Electrical Engineering Technology, Higher Colleges of Technology, Abu Dhabi P.O. Box 25035, United Arab EmiratesDepartment of Electrical Engineering Technology, Higher Colleges of Technology, Abu Dhabi P.O. Box 25035, United Arab EmiratesDepartment of Electrical Engineering Technology, Higher Colleges of Technology, Abu Dhabi P.O. Box 25035, United Arab EmiratesDepartment of Electrical Engineering Technology, Higher Colleges of Technology, Abu Dhabi P.O. Box 25035, United Arab EmiratesDirection of Research and Innovation, Berger-Levrault, 34130 Mauguio, FranceDrowning is among the most common reasons for children’s death aged one to fourteen around the globe, ranking as the third leading cause of unintentional injury death. 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, which alert lifeguards or parents to perform manual rescues, and detection and rescue-based systems, which integrate detection with automatic rescue mechanisms. 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 monitor their physical parameters. We explore the advantages and limitations of each approach. Additionally, we highlight technical challenges and unresolved issues related to this domain, such as data imbalance, accuracy, privacy concerns, and integration with rescue systems. We also identify future research opportunities, emphasizing the need for more advanced AI models, uniform datasets, and better integration of detection with autonomous rescue mechanisms. This study aims to provide a critical resource for researchers and practitioners, facilitating the development of more effective systems to enhance water safety and minimize drowning incidents.https://www.mdpi.com/2078-2489/15/11/721drowningdetectionpreventionrescueswimming pool safetyartificial intelligence |
| spellingShingle | Maad Shatnawi Frdoos Albreiki Ashwaq Alkhoori Mariam Alhebshi Anas Shatnawi Advances and Challenges in Automated Drowning Detection and Prevention Systems Information drowning detection prevention rescue swimming pool safety artificial intelligence |
| title | Advances and Challenges in Automated Drowning Detection and Prevention Systems |
| title_full | Advances and Challenges in Automated Drowning Detection and Prevention Systems |
| title_fullStr | Advances and Challenges in Automated Drowning Detection and Prevention Systems |
| title_full_unstemmed | Advances and Challenges in Automated Drowning Detection and Prevention Systems |
| title_short | Advances and Challenges in Automated Drowning Detection and Prevention Systems |
| title_sort | advances and challenges in automated drowning detection and prevention systems |
| topic | drowning detection prevention rescue swimming pool safety artificial intelligence |
| url | https://www.mdpi.com/2078-2489/15/11/721 |
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