Infant Drowning Prevention: Insights from a New Ecological Psychology Approach

Effect of baby swimming lessons on infants' avoidance of bodies of water

Despite the popularity of infant swimming programs, no evidence exists to determine whether they influence infants' judgments and behavior when confronted with bodies of water. We conducted two separate studies examining if the total number of swimming sessions an infant participated in predicted whether they avoided a body of water they could enter via an edge (Study 1-Water Cliff: n = 101 infants) or a slope (Study 2-Water Slope: n = 77 infants). The results revealed a significant interaction between number of sessions and type of entry into the water. Infants who participated in 10 or more sessions were more likely to avoid falling on the edge leading into the water but entered the water significantly more if they could access it via a slope. These findings suggest that while experience in baby swimming programs can promoted more adaptive behaviors on drop-offs leading into bodies of water, sloped entries may heighten drowning risks for young children with greater familiarity with water. Because we tested the two groups of infants in different countries, further research is warranted to determine if cultural differences in child rearing practices or variations in the content and/or teaching of the swimming programs might explain these intriguing findings.

Deep Learning and 5G and Beyond for Child Drowning Prevention in Swimming Pools

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.