Animal Condition Monitoring via Frequency Spectrum Energy Analysis
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Solution Overview
Problem
Existing animal condition monitoring systems using movement sensors lack accuracy in determining the current condition of animals due to overlapping movement patterns among different activities.
Innovation Solution
A method and system that analyze movement signals by converting them into frequency spectra, subdividing into subregions, determining energy levels in each subregion, and comparing these energy levels with expectation values to determine the most probable animal condition using probability distribution data, allowing for accurate condition assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If movement sensors are used to monitor animal condition, then monitoring capability is provided, but accuracy in determining current condition deteriorates due to overlapping movement patterns
Solution Approach 1:
The frequency spectrum is segmented into multiple frequency subregions, and energy is analyzed separately in each subregion. This segmentation allows the system to capture distinct movement pattern characteristics across different frequency ranges, enabling accurate differentiation between various animal conditions that would otherwise overlap in the time domain.
Solution Approach 2:
The invention transforms the movement signal from the time domain to the frequency domain using Fourier transformation. This dimensional change allows the system to analyze movement patterns in terms of frequency components and energy distribution, providing a new perspective that resolves the ambiguity of overlapping movement patterns in the time domain.
2Measurement precision
If frequency spectrum analysis with multiple subregions is performed, then condition determination accuracy is improved, but computational complexity increases
Solution Approach 1:
The frequency spectrum is divided into multiple subregions, with energy calculation performed independently in each subregion. This segmentation strategy balances accuracy improvement with computational efficiency by processing smaller frequency ranges separately rather than analyzing the entire spectrum as one complex operation.
Solution Approach 2:
The system calculates energy only in selected frequency subregions rather than processing the entire frequency spectrum in detail. This partial action approach provides sufficient accuracy for condition determination while reducing the overall computational burden and complexity of the signal processing system.
Data Source
AI summary
The invention relates to a method and system for determining the condition of an animal. Movements of the animal are measured during a defined time period. The movements are converted into a movement signal that represents the measured movements. A frequency spectrum of the movement signal is determined. The frequency spectrum is subdivided into a plurality of frequency subregions. For a plurality of the frequency subregions, per frequency subregion, the amount of energy in the respective frequency subregion is determined. For each of a plurality of frequency subregions, the determined amount of energy in the respective frequency subregion is compared with each of a plurality of expectation values for the amount of energy in the frequency subregion, each expectation value belonging to one condition of a plurality of conditions of the animal, for determining the current condition of the animal.