{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T16:04:25Z","timestamp":1787241865512,"version":"build-2736575974"},"reference-count":44,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Agencia Estatal de Investigaci\u00f3n of Ministerio de Ciencia e Innovaci\u00f3n of Spain","award":["PID2019-108713RB-C51 MCIN\/AEI\/10.13039\/501100011033"],"award-info":[{"award-number":["PID2019-108713RB-C51 MCIN\/AEI\/10.13039\/501100011033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Drowning is a major health issue worldwide. The World Health Organization\u2019s global report on drowning states that the highest rates of drowning deaths occur among children aged 1\u20134 years, followed by children aged 5\u20139 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.<\/jats:p>","DOI":"10.3390\/s22197684","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:50:01Z","timestamp":1665449401000},"page":"7684","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Deep Learning and 5G and Beyond for Child Drowning Prevention in Swimming Pools"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9932-9428","authenticated-orcid":false,"given":"Juan Carlos","family":"Cepeda-Pacheco","sequence":"first","affiliation":[{"name":"Department of Network Engineering, BarcelonaTech (UPC) University, 08860 Castelldefels, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6901-3817","authenticated-orcid":false,"given":"Mari Carmen","family":"Domingo","sequence":"additional","affiliation":[{"name":"Department of Network Engineering, BarcelonaTech (UPC) University, 08860 Castelldefels, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"ref_1","unstructured":"Meddings, D., Altieri, E., Bierens, J., Cassell, E., Gissing, A., and Guevarra, J. 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