Prevention Over Detection: How Machine Learning Saves Lives

According to the World Health Organization (WHO), drowning is the 3rd leading cause of unintentional injury death worldwide.

According to the U.S. Consumer Product Safety Commission (CPSC), an average of 357 children under the age of 15 fatally drowned in pool- or spa-related incidents each year between 2020 and 2022. The CPSC's 2025 report further found that from 2022 through 2024, an average of 73% of children treated in emergency departments for pool- or spa-related nonfatal drowning injuries were younger than 5 years of age.

CPSC In Australia, the Royal Life Saving Society's 2024 National Drowning Report recorded 35 pool drowning deaths — a 21% increase from the prior year. Swimming pool drownings have spiked across the EU, as well.

Clearly, preventing swimming pool drownings is a global problem requiring new solutions. We must shift from detection to prevention—to respond quickly before a drowning event unfolds.

Current Drowning Detection is Inherently Flawed

Drowning happens quickly and quietly. It doesn’t look like what most people expect. The early signs of distress are difficult to identify and easy to miss. And this is the danger.

A lifeguard or nearby swimmer may notice something, but uncertainty can lead them to wait for confirmation. As too many tragedies reveal, relying on human sight and comprehension capabilities alone hinders adequate responses.

A detection-focused strategy will not help us reach our mission of eliminating swimming pool drownings.

Overhead Cameras

Security cameras have become more prevalent throughout the swimming pool industry. These cameras primarily serve as a security function, and perhaps as a tool for managers to review pool usage. However, standard CCTV cameras are not used for drowning prevention, nor are they capable of serving in such a capacity. They have significant limitations, chief among them being that they cannot see through water disturbances.

Without image-correction capability, CCTV cameras cannot prevent swimming pool drownings.

Lifeguards

Lifeguards have served as first responders to swimmers in distress for decades. However, they can’t see underwater and must juggle many tasks simultaneously, including maintaining order in the pool.

Lynxight delivers cutting-edge technology that empowers lifeguards to do their job successfully. Furthermore, its analytics capabilities enable pool managers to make the most efficient use of their staff and other resources, a critical advantage today, as the world experiences a lifeguard labor shortage.

Lynxight Aquatic Safety & Analytics Service

Lynxight offers a new way to manage safety risks and put emphasis on prevention over detection. By utilizing the power of artificial intelligence and machine learning we’re able to overcome the inherent deficiencies in today’s response to swimming pool safety.

Artificial Intelligence (AI)

AI technology transforms standard CCTV cameras into smart cameras. Our novel software digitizes and analyzes the images captured by the CCTV cameras, effectively seeing through the water and allowing the system to track swimmers doing everything from diving to splashing around. Surface water disturbances, such as waves, ripples and glare no longer pose a problem.

Machine Learning

Machine learning is an AI application that enables a system to learn and improve itself without reprogramming. Thousands of bits of data, such as swimmer behavior profiling, distress situations and drownings collected from pools worldwide are amassed to create the special algorithms used by our drowning prevention tool.

Artificial technology and machine learning combine to quickly identify instinctive drowning response behavior. Once this happens, an alert is sent to the lifeguard, who can immediately take appropriate action to save a swimmer in distress.