1,100 research outputs found

    Deep learning and 5G and beyond for child drowning prevention in swimming pools

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    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.Peer ReviewedPostprint (published version

    The Real-Time Classification of Competency Swimming Activity Through Machine Learning

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    Every year, an average of 3,536 people die from drowning in America. The significant factors that cause unintentional drowning are people’s lack of water safety awareness and swimming proficiency. Current industry and research trends regarding swimming activity recognition and commercial motion sensors focus more on lap swimming utilized by expert swimmers and do not account for freeform activities. Enhancing swimming education through wearable technology can aid people in learning efficient and effective swimming techniques and water safety. We developed a novel wearable system capable of storing and processing sensor data to categorize competitive and survival swimming activities on a mobile device in real-time. This paper discusses the sensor placement, the hardware and app design, and the research process utilized to achieve activity recognition. For our studies, the data we have gathered comes from various swimming skill levels, from beginner to elite swimmers. Our wearable system uses angle-based novel features as inputs into optimal machine learning algorithms to classify flip turns, traditional competitive strokes, and survival swimming strokes. The machine-learning algorithm was able to classify all activities at .935 of an F-measure. Finally, we examined deep learning and created a CNN model to classify competitive and survival swimming strokes at 95% ac- curacy in real-time on a mobile device

    Automatic Analysis of People in Thermal Imagery

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    The Visible Behaviour of Drowning Persons: A Pilot Observational Study Using Analytic Software and a Nominal Group Technique

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    Although drowning is a common phenomenon, the behaviour of drowning persons is poorly understood. The purpose of this study is to provide a quantitative and qualitative analysis of this behaviour. This was an observational study of drowning videos observed by 20 international experts in the field of water safety. For quantitative analysis, each video was analysed with Lince observation software by four participants. A Nominal Group Technique generated input for the qualitative analysis and the two principal investigators conducted a post-hoc analysis. A total of 87.5% of the 23 videos showed drowning in swimming pools, 50% of the drowned persons were male, and 58.3% were children or teenagers. Nineteen persons were rescued before unconsciousness and showed just the beginning of downing behaviour. Another five were rescued after unconsciousness, which allowed the observation of their drowning behaviour from the beginning to the end. Significant differences were found comparing both groups regarding the length of disappearances underwater, number, and length of resurfacing (resp. p = 0.003, 0.016, 0.005) and the interval from the beginning of the incident to the rescue (p = 0.004). All persons drowned within 2 min. The qualitative analysis showed previously suggested behaviour patterns (immediate disappearance n = 5, distress n = 6, instinctive drowning response n = 6, climbing ladder motion n = 3) but also a striking new pattern (backward water milling n = 19). This study confirms previous assumptions of drowning behaviour and provides novel evidence-based information about the large variety of visible behaviours of drowning persons. New behaviours, which mainly include high-frequency resurfacing during a struggle for less than 2 min and backward water milling, have been recognised in this study

    Computer Vision Based Object Detection and Tracking in Micro Aerial Vehicles

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    ­­­­The ultimate goal of Computer Vision is to instruct a computer to understand and interpret visual signals and images in real time and to instruct a computer to react to the environment around them. In this work, we describe a system that allows a micro aerial vehicle (MAV), equipped with an onboard camera, to detect and track a moving target object. In an alternative implementation, the MAV instead searches the environment for the target object and flies to it. Due to the limited capability of the drone’s integrated processor, image processing is performed by a ground-based computer that also determines the necessary flight corrections and communicates them to the vehicle. The complete system, comprised of the MAV, off-board computer, and software, operates autonomously, a necessary condition for many of the applications for which such systems may be useful
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