ArticleSensors (Basel, Switzerland)2022
Deep Learning and 5G and Beyond for Child Drowning Prevention in Swimming Pools.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Promoting Drowning Preventive Behaviors Among Caregivers of Children Under 5 Years Through a Health Belief Model-Based Educational Program in Thailand: A Community Study.Healthcare (Basel, Switzerland) · 2026Article
- A Pool Drowning Detection Model Based on Improved YOLO.Sensors (Basel, Switzerland) · 2025Article
- Next-Generation swimming pool drowning prevention strategy integrating AI and IoT technologies.Heliyon · 2024Review
- Enhancing Water Safety: Exploring Recent Technological Approaches for Drowning Detection.Sensors (Basel, Switzerland) · 2024Review
- Artificial Neural Networks for IoT-Enabled Smart Applications: Recent Trends.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Drowning is a major health issue worldwide. The World Health Organization's global report on drowning states that the highest rates of drowning deaths occur among children aged 1-4 years, followed by children aged 5-9 years. Young children can drown silently in as little as 25 s, even in the shallow end or in a baby pool. The report also identifies that the main risk factor for children drowning is the lack of or inadequate supervision. Therefore, in this paper, we propose a novel 5G and beyond child drowning prevention system based on deep learning that detects and classifies distractions of inattentive parents or caregivers and alerts them to focus on active child supervision in swimming pools. In this proposal, we have generated our own dataset, which consists of images of parents/caregivers watching the children or being distracted. The proposed model can successfully perform a seven-class classification with very high accuracies (98%, 94%, and 90% for each model, respectively). ResNet-50, compared with the other models, performs better classifications for most classes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.