Mobile Sensor Signal Evaluation Using Hierarchical Histograms
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Solution Overview
Problem
Existing methods for processing sensor signals on mobile devices face challenges in identifying characteristic data patterns due to limited storage capacity and computing power, particularly in real-time data analysis and outlier detection, where kernel density estimation requires high computational resources and storage.
Innovation Solution
A method using a hierarchical distribution of reference data values with varying resolutions, allowing for efficient data evaluation by assigning data values to sections with relative frequencies, enabling outlier detection and classification without external resources, and updating the reference distribution with new data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If kernel density estimation is used for sensor signal processing, then measurement precision and classification accuracy are improved, but device complexity and computational resource requirements increase significantly
Solution Approach 1:
The patent segments the continuous data range into discrete bins or intervals, transforming the continuous kernel density estimation problem into a discrete histogram-based approach. This segmentation reduces computational complexity by replacing complex integration operations with simple frequency counting and arithmetic operations, making the solution feasible for mobile devices with limited resources while preserving classification accuracy
Solution Approach 2:
The patent uses simplified histogram data structures instead of complex kernel density models, employing lightweight computational approaches that consume fewer resources. The histogram-based method uses simple arrays to store frequency counts, replacing memory-intensive kernel functions with efficient lookup tables, thereby reducing both computational and memory requirements
2Measurement precision
If high-resolution reference distribution is used, then measurement precision is improved, but storage capacity requirements increase
Solution Approach 1:
The patent divides the reference distribution into discrete bins with configurable resolution, allowing the system to balance between precision and storage. By segmenting the continuous distribution into manageable intervals, the patent enables efficient storage using compact histogram structures while maintaining sufficient resolution for accurate outlier detection
Solution Approach 2:
The patent dynamically adjusts the bin resolution parameter based on available storage capacity and performance requirements. By changing the resolution parameter, the system can adapt the reference distribution to fit within memory constraints while preserving the ability to detect meaningful outliers, effectively trading off precision for storage efficiency when necessary
3Productivity
If real-time data analysis is performed, then productivity is improved, but use of energy and computational power increase
Solution Approach 1:
The patent replaces computationally intensive kernel density estimation operations with simpler arithmetic operations on histogram data. By substituting complex mathematical computations with efficient array lookups and basic arithmetic, the patent enables real-time processing on mobile devices with limited processing power and energy resources, maintaining productivity while reducing energy consumption
Data Source
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AI summary
The invention relates to a method for processing sensor signals from a sensor (312) using a mobile device (300). The method comprises providing a reference distribution (220, 308) of reference data values (202) characteristic of a physical object's status, acquiring a plurality of data values (310) of the physical object from sensor signals of the sensor (312) using the mobile device (300), and evaluating the acquired data values (310) using the mobile device (300). The evaluation further comprises determining an overall average probability using data-value-specific average probabilities of the acquired data values (310) and checking whether the acquired data values (310) are characteristic of the physical object's status.If the data values (310) are characteristic of the status of the physical object, the procedure further includes generating a confirmation signal that the acquired data values (310) are characteristic of the status of the physical object, and updating the reference distribution (220, 308) stored in the memory (306) of the mobile device (300) with the acquired data values (310).