AI Water Safety Camera with Wireless Extender for Early Detection
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Solution Overview
Problem
Current water safety devices that monitor bodies of water for potential drowning situations are often located in the water, detect disturbances after entry, and suffer from false alarms and poor integration with remote notification systems, leading to delayed detection and alerting.
Innovation Solution
A device and system that uses artificial intelligence to detect human presence outside the water, providing earlier alerts and improved surveillance with a camera mounted outside the water, utilizing Wi-Fi and Zigbee networks for extended communication and notification to smartphones, and includes features like quick pairing and smart lock integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If water safety devices are located in the water to detect disturbances, then detection can occur at the time drowning begins, but detection is delayed and only provides near-vicinity alarms
Solution Approach 1:
The camera device is positioned outside the water to capture images of the pool area before drowning incidents occur. The system performs preliminary detection of human presence and suspicious behavior patterns (such as struggling movements or unusual positioning) before the actual drowning event, enabling early warning and intervention rather than reactive detection after entry into water.
2Reliability
If traditional water monitoring devices are used, then detection occurs after water entry, but false alarm rates are high and remote notification is poor
Solution Approach 1:
The system introduces a communication module as an intermediary between the camera device and remote user devices. This module processes detected suspicious patterns, determines whether they constitute actual drowning risks, and selectively notifies remote users through optimized communication protocols. The intermediary filters false alarms by analyzing multiple parameters (human presence confirmation, suspicious behavior patterns, location within pool area) before triggering notifications, thereby improving alarm reliability and reducing unnecessary alerts.
3Measurement precision
If AI processing is applied to all captured images, then human presence detection is accurate, but processing time and computational resources increase
Solution Approach 1:
The image processing is segmented into multiple stages: first, the camera captures images of the pool area; second, the system identifies regions containing potential human figures using lightweight detection algorithms; third, AI processing is applied only to these specific regions of interest to confirm human presence and analyze behavior patterns. This segmentation reduces the total computational load compared to processing entire images, maintaining detection accuracy while improving processing speed and enabling real-time monitoring.
Data Source
AI summary
Systems and methods for improving water safety are provided. The methods include various features based on monitoring the area around a pool. Benefits are gained by applying artificial intelligence algorithms, as well as various techniques for improving processing speed and eliminating false alarms. A system for promoting water safety includes: a camera device configured to capture images in a monitored area around or above a body of water, the camera device configured for mounting to a structure outside the water; a computing device configured to process the captured images and to apply artificial intelligence for human recognition to the processed captured images; a mobile device configured to receive a notification if a human is detected in the captured images; and an extender in wireless communication with the camera device that extends a wireless network to the camera device.