Neural Network Drowning Detection via Transfer Learning
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Solution Overview
Problem
Current devices for detecting drowning individuals are limited in their ability to quickly identify and alert for drowning risks outside of specific areas, such as swimming pools, and often result in false positives or require the individual to have sunk before detection can occur.
Innovation Solution
A device utilizing an artificial neural network with multiple layers, pre-trained on standard image and video data, and specialized through learning transfer with drowning scenario data, to identify drowning situations in real-time from video streams, capable of sending alerts and providing location information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional drowning detection devices are used, then detection can be performed in specific areas like swimming pools, but the monitoring area is limited and detection speed is insufficient for early warning
Solution Approach 1:
The patent applies universality by developing a drowning detection system that can operate in multiple environments (swimming pools, open water, beaches) rather than being limited to a single location. The camera system and neural network algorithm are designed to function universally across different water bodies, enabling early warning capability in diverse settings while maintaining high detection accuracy through environment-adaptive AI processing.
2Device complexity
If simple AI algorithms are used for detecting drowned individuals, then the device structure is simplified, but false alerts increase when swimmers are holding their breath at the bottom of the pool
Solution Approach 1:
The patent implements feedback mechanisms through continuous video analysis where the neural network processes multiple frames sequentially, comparing movements across time. The system uses feedback from the video stream to distinguish between genuine drowning situations and false positives like swimmers holding their breath, thereby improving reliability while maintaining manageable system complexity through intelligent algorithmic processing.
3Reliability
If detection is based on individual monitoring devices worn by swimmers, then personal monitoring is enabled, but area monitoring capability is lost and response time is delayed
Solution Approach 1:
The patent applies preliminary action by deploying fixed camera systems that continuously monitor water surfaces before drowning incidents occur. The neural network algorithm is pre-trained to recognize early signs of drowning and can detect situations before the individual has sunk or lost consciousness, enabling early warning and reducing response time compared to reactive wearable devices that only monitor after the incident has started.
4Device complexity
If detection relies on identifying swimmers who have sunk to the bottom of the pool, then detection is simplified, but the system cannot detect drowning risks before the individual sinks
Solution Approach 1:
The patent implements preliminary action through continuous video streaming and real-time neural network analysis that detects drowning signs before the individual sinks to the bottom. The system monitors water surface behavior, body movements, and swimming patterns in advance, enabling early warning capability that prevents delayed detection until sinking occurs, thereby reducing the critical response time window.
Data Source
AI summary
The present invention relates to a device (2) for detecting drowning individuals or individuals in a situation presenting a risk of drowning, comprising at least one program of codes that are executable on one or more processing hardware components such as a microprocessor, the program being stored in memory in at least one readable medium and implementing an artificial neural network (20) having an automatic learning architecture composed of several layers, the artificial neural network (20) being pre-trained on image data from at least one standard non-specific database, the program being characterized in that the neural network is further trained a second time by learning transfer on image data from videos of simulated or real drowning situations or situations presenting a risk of drowning, the trained program being configured by this learning transfer to identify, preferably in real time, drowning situations or situations presenting a risk of drowning based on new image data provided.