Integrating AI and IoT for Drowning Prevention in Pools

By Muhammad OsamaReviewed by Susha Cheriyedath, M.Sc.Aug 9 2024

In a recent article published in the journal Heliyon, researchers comprehensively explored how advanced technologies can help prevent drowning, a major global public health issue. They explored how artificial intelligence (AI), and embedded systems can improve drowning detection and prevention.

Background

Drowning is a serious global concern, causing around 236,000 deaths in 2019 alone. It is the second leading cause of unintentional injury-related deaths after falls, according to the Global Burden of Disease (GBD) 2019 estimates. Mainly, these types of deaths occur in low- and middle-income countries, highlighting the need for effective and accessible prevention strategies.

Drowning incidents often happen quickly and silently, making them difficult to detect, even for trained lifeguards. Traditional methods have focused on improving hardware and raising awareness, but advanced technology, such as AI and embedded systems, offers new ways to monitor and intervene in real-time.

Previously, limited computational power and processing speed hindered the development of effective drowning prevention systems. However, the 21st century has seen significant progress in integrating AI across various fields, including healthcare, finance, and transportation. AI encompasses techniques like machine learning, deep learning, natural language processing, and computer vision. These advancements have improved image recognition speed and accuracy, making AI crucial for drowning prevention.

About the Research

In this paper, the authors explored the application of advanced technologies, such as AI, embedded systems, computer vision, and the Internet of Things (IoT), in drowning prevention. They aimed to overcome the limitations of traditional drowning prevention systems by leveraging these emerging technologies to enhance the efficiency and reliability of drowning detection and prevention.

The researchers investigated various approaches, including embedded systems, sensors, and computer vision techniques. For example, embedded systems can incorporate hardware sensors to enhance safety within venues. Specifically, sensor-based systems using ultrasonic sensors, radar, and wearable devices monitor swimmers' movements and physiological data, such as heart rate and oxygen levels.

Ultrasonic sensors, in particular, can detect drowning incidents, trigger alarms, and deploy devices to assist in rescues. Additionally, image processing combined with accelerometers, pulse and pressure sensors, and light amplification by stimulated emission of radiation (LASER) light-dependent resistors (LDRs) has been used to monitor physiological signals and detect potential drownings.

Research Findings

The integration of computer vision and deep learning algorithms significantly improved drowning detection speed and accuracy. The authors examined object detection models such as You Only Look Once (YOLO), faster region-based convolutional neural networks (Faster R-CNN), and mask region-based CNN (Mask R-CNN). These models analyzed video footage from overhead or underwater cameras to identify swimmers in distress and assess their movements and postures. Notably, they demonstrated high accuracy and reliability, even in challenging environments.

Additionally, the IoT facilitated rapid alert transmission upon detecting drowning incidents, enabling prompt notification to lifeguards or other personnel. The study proposed IoT-based systems that combined sensor data, cloud computing, and mobile applications to monitor swimming pool conditions, detect drowning events and notify relevant personnel in real-time.

The study highlighted the transformative potential of embedded systems, computer vision, and IoT in drowning prevention. Embedded systems provide real-time monitoring of swimmers' physiological signals. Computer vision offered advanced image recognition for automated detection of distressed swimmers. The IoT connected devices and transmitted data rapidly, creating real-time alert systems. Using high-speed networks like 5G, these systems could send alerts to lifeguards, family members, or emergency responders, supporting prompt rescue efforts.

Applications

This research has significant implications for the development of comprehensive drowning prevention systems. The integration of embedded systems, computer vision, and IoT technology can create a multi-layered approach to drowning detection and response. These systems can continuously monitor swimming environments, identify potential drowning incidents, and trigger immediate alerts, thereby increasing the chances of successful rescues and saving lives.

The authors also highlighted the potential applications of these technologies in various settings, from indoor swimming pools to open water environments like beaches and lakes. The adaptability of these systems to different scenarios, such as handling environmental factors and accommodating diverse swimming behaviors, is crucial for their widespread adoption and effectiveness.

Conclusion

In summary, the advanced technology-based novel solution significantly improved drowning detection and prevention. It has the potential to transform water safety by providing real-time monitoring, early warnings, and quick emergency responses, ultimately saving lives and reducing drowning incidents worldwide.

Moving forward, the researchers recommended further refining and expanding the system using underwater sensor networks, advanced deep learning algorithms, and seamless IoT integration. These efforts could lead to stronger and more adaptable solutions. Additionally, collaboration with policymakers and water safety organizations could support the widespread adoption and use of these life-saving technologies.