IoT Proximity Danger Detection Through Dynamic Subject-Object Analysis
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Solution Overview
Problem
Existing child monitoring systems struggle to proactively identify real-time proximity dangers for subjects, such as small children or pets, as they rely on pre-defined danger zones that fail to account for dynamic environmental risks.
Innovation Solution
A system that utilizes IoT devices to analyze real-time video feeds, detect and classify subjects and objects, correlate risk factors with activities, and determine relative positions to dynamically assess proximity dangers, sending notifications when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-defined danger zones are used for child monitoring, then the system is simple to implement, but it fails to identify dynamic environmental risks and real-time proximity dangers
Solution Approach 1:
The system transitions from static pre-defined danger zones to dynamic real-time danger assessment by continuously analyzing subject-object distances, object classifications, and activity contexts. The danger zone boundaries and risk levels are dynamically adjusted based on current environmental conditions and subject behavior patterns.
Solution Approach 2:
The patent replaces the mechanical/simple approach of fixed spatial zones with an intelligent system that uses image processing, machine learning classification, and computational geometry to assess dangers. The system substitutes physical barrier definitions with virtual intelligent assessment layers.
2Measurement precision
If real-time video analysis and object classification are performed, then proximity danger detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-classifying objects into danger categories and pre-establishing risk profiles for different object types. This allows the real-time processing to focus only on calculating distances and assessing current danger levels, rather than analyzing all visual data from scratch.
Solution Approach 2:
The system applies partial action by selectively analyzing only the portions of video data that are currently relevant - specifically focusing on detected subjects and objects rather than processing the entire video stream uniformly. This reduces computational burden while maintaining precision for critical safety assessments.
Data Source
AI summary
An embodiment for detecting the danger of an object in close proximity of a subject is provided. The embodiment may include receiving real-time data from one or more IoT devices in a surrounding environment. The embodiment may also include detecting and classifying one or more subjects and one or more objects in an image from the one or more IoT devices. The embodiment may further include identifying one or more risk factors associated with each object. The embodiment may also include correlating the one or more risk factors associated with each object with the one or more subjects in the image. The embodiment may further include identifying relative positions of the one or more subjects and the one or more objects in the image. The embodiment may also include in response to determining a current position of at least one subject is dangerous, notifying a user of the danger.