Floor Identification via Barometric Clustering and Feedback
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Solution Overview
Problem
Existing emergency response systems struggle to accurately locate individuals within multi-story buildings during emergencies, relying on outdated infrastructure and lacking real-time feedback for precise floor identification, which hampers rescue efforts and safety.
Innovation Solution
A computer-implemented method using device clustering algorithms and first responder feedback to determine the approximate floor of a device within a building by annotating cluster centroids with regional floor-height values, re-computing clusters based on feedback, and integrating GPS and barometric pressure data for accurate location determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional emergency response systems are used to locate individuals in multi-story buildings, then the system infrastructure is simple and easy to implement, but the floor identification accuracy is poor and rescue time is extended
Solution Approach 1:
The patent adds vertical dimension (elevation/floor level) to the traditional 2D geospatial location system. By incorporating barometric pressure data to determine elevation and mapping it to floor levels, the system transforms standard 2D location into 3D spatial information, enabling precise floor identification within multi-story buildings.
Solution Approach 2:
The system implements feedback loops where location data from multiple devices is continuously collected, processed through clustering algorithms, and refined based on identified patterns. The clustering results are used to update and improve future floor identification accuracy, creating a self-enhancing system that becomes more precise over time.
2Measurement precision
If device clustering algorithms with feedback loops are implemented to improve floor identification accuracy, then the measurement precision is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The system performs self-calibration and self-improvement by automatically collecting location data from multiple devices, executing clustering algorithms to identify patterns, and using these patterns to refine future floor identification. The feedback loop enables the system to automatically learn and adapt without requiring manual intervention or reconfiguration.
Solution Approach 2:
The system pre-computes cluster centroids and regional floor-height values from historical location data before actual emergency response situations occur. This preliminary processing creates a ready-to-use reference framework that enables rapid floor identification during critical moments without requiring complex real-time calculations.
3Measurement precision
If regional floor-height values are annotated to cluster centroids and feedback is incorporated, then the floor identification precision is improved, but the data processing time and computational resources increase
Solution Approach 1:
The system pre-computes cluster centroids and annotates them with regional floor-height values using historical location data before emergency situations occur. This preliminary processing creates a ready-to-use reference framework that enables rapid floor identification during critical moments without requiring complex real-time calculations.
Solution Approach 2:
The system divides the service area into regional clusters, each with its own centroid and floor-height characteristics. By localizing the computational model to specific geographic regions rather than using a single global model, the system reduces computational complexity while maintaining high precision for local floor identification.
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 solution enhances real-time floor identification accuracy, reduces rescue time, and improves safety for both individuals in need of assistance and emergency responders by continuously refining inter-floor height mappings through feedback loops.
Implementation Method 1
A mobile device's determined location and barometric pressure data may be used to identify a floor level of a structure
Data Source
AI summary
Known geospatial device coordinates are clustered using a clustering algorithm into device clusters with cluster centroids. Each device cluster corresponds to a geographical location. Each cluster centroid is annotated with a regional floor-height value of the respective geographical location. Current device data of a device, including geographic location and elevation, are received. An approximate current floor upon which the first device is located is determined using the elevation of the first device and the annotated regional floor-height value of a closest cluster centroid, the closest cluster centroid determined based, at least in part, on the geographic location of the first device. An individual is directed to the device's geographic location and approximate current floor. The device clusters are re-computed based upon feedback from the individual regarding the device's actual floor.