Vibration Classifier Segmentation for Break-in Detection
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Solution Overview
Problem
Existing vibration detection systems struggle to accurately differentiate between harmless activities and break-in attempts, leading to false alarms or missed detections, especially when residents are present and windows or doors are open.
Innovation Solution
A method involving discontinuous analysis of vibration signals, split into sub-periods, calculates variation indicators for each component to classify events as break-ins or harmless activities, using a combination of threshold-based and machine learning approaches, and optionally incorporating frequency analysis to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vibration sensors are used to detect break-ins, then detection capability is improved, but false alarms increase
Solution Approach 1:
The vibration signal is divided into multiple sub-periods, and each sub-period is analyzed separately to calculate variation indicators. This segmentation allows the system to distinguish between continuous harmless vibrations and discrete break-in attempts, reducing false alarms while maintaining detection capability.
Solution Approach 2:
The system calculates variation indicators based on changes in vibration parameters across different sub-periods. By analyzing the variation in vibration characteristics rather than absolute values, the system can differentiate between normal activities and break-in attempts, improving reliability while reducing false positives.
2Reliability
If indoor motion sensors are used when residents are home, then detection capability is improved, but system reliability deteriorates
Solution Approach 1:
The vibration signal is segmented into sub-periods, allowing the system to analyze patterns during different time intervals. This enables the system to distinguish between vibrations caused by residents' normal activities and actual break-in attempts, maintaining high reliability even when residents are present.
Solution Approach 2:
The system continuously monitors vibration patterns and uses feedback from the variation indicators to adjust its detection threshold. This adaptive feedback mechanism allows the system to learn normal resident behavior patterns and maintain high detection accuracy without false alarms when residents are home.
3Reliability
If continuous vibration monitoring is performed, then detection coverage is improved, but power consumption increases
Solution Approach 1:
Instead of continuous processing, the system performs vibration monitoring in periodic intervals, dividing the monitoring period into sub-periods. This periodic action maintains detection coverage while significantly reducing power consumption by keeping the processing unit in low-power states between measurement intervals.
Solution Approach 2:
The monitoring process is segmented into discrete measurement periods with sub-periods for analysis. This segmentation allows the system to maintain comprehensive detection coverage while optimizing power consumption by processing vibrations in manageable, power-efficient intervals rather than continuously.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces false alarms and improves detection accuracy by reliably distinguishing between different types of vibrations, including glass breaking and nail-based attacks, while maintaining low power consumption.
Implementation Method 1
These are used for detecting vibrations that occur when a break-in attempt occurs
Data Source
Figure 1~2B
Figure 3~5
Figure 6~7C
AI summary
It is provided a method for classifying vibrations detected in a structure of a building. The method is performed in a vibration classifier and comprising the steps of: determining a measurement period of a vibration signal; splitting the measurement period in a plurality of sequential sub-periods; calculating, for each one of the sub-periods, a variation indicator of at least one component of the vibration signal; and classifying a source of the vibration signal based on the variation indicators.