Enhancing Water Safety: Exploring Recent Technological Approaches for Drowning Detection

Abstract

Drowning poses a significant threat, resulting in unexpected injuries and fatalities. To promote water sports activities, it is crucial to develop surveillance systems that enhance safety around pools and waterways. This paper presents an overview of recent advancements in drowning detection, with a specific focus on image processing and sensor-based methods. Furthermore, the potential of artificial intelligence (AI), machine learning algorithms (MLAs), and robotics technology in this field is explored. The review examines the technological challenges, benefits, and drawbacks associated with these approaches. The findings reveal that image processing and sensor-based technologies are the most effective approaches for drowning detection systems. However, the image-processing approach requires substantial resources and sophisticated MLAs, making it costly and complex to implement. Conversely, sensor-based approaches offer practical, cost-effective, and widely applicable solutions for drowning detection. These approaches involve data transmission from the swimmer’s condition to the processing unit through sensing technology, utilising both wired and wireless communication channels. This paper explores the recent developments in drowning detection systems while considering costs, complexity, and practicality in selecting and implementing such systems. The assessment of various technological approaches contributes to ongoing efforts aimed at improving water safety and reducing the risks associated with drowning incidents.

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

“…RESULT AND DISCUSSION Following an exploration of the methodologies employed in this research, our focus now shifts to presenting and analyzing the results obtained through the utilization of the Learning Focal Point (LFP) algorithm. A key aspect of this analysis involves a comparative examination with the outcomes derived from employing the widely utilized MaxPooling technique [67], [68], [69]. This comparative evaluation aims to underscore the significance of the Learning Focal Point algorithm in enhancing the performance of neural networks.…”