Respiratory Distress Index Pattern Matching for Pathogen Prediction
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Solution Overview
Problem
Current methods in livestock farming cannot predict the specific respiratory pathogen causing distress in farm animals based on sound analysis, despite the potential of sound-based Precision Livestock Farming techniques for monitoring respiratory issues.
Innovation Solution
A method involving the recording and digital conversion of sounds, filtering background noise, calculating the Respiratory Distress Index (RDI), and comparing the RDI patterns with a stored database to identify pathogen-specific patterns, allowing for the prediction of the causative pathogen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sound-based monitoring is used to detect respiratory distress, then early detection capability is improved, but the ability to identify specific pathogen type remains insufficient
Solution Approach 1:
The patent segments the respiratory distress signal into multiple acoustic parameters (frequency, amplitude, temporal patterns, spectral features) that are analyzed independently and then integrated. This segmentation allows the system to capture different aspects of the distress signal that collectively enable pathogen identification, resolving the contradiction between early detection and precise pathogen identification.
Solution Approach 2:
The patent transforms the raw sound signal into multiple derived parameters including frequency spectrum, amplitude modulation, temporal patterns, and spectral entropy. By changing the representation parameters from simple sound detection to multi-dimensional acoustic feature analysis, the system achieves both early detection and specific pathogen identification.
2Measurement precision
If multiple acoustic parameters are analyzed to improve pathogen identification, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary signal processing steps including noise filtering, segmentation, and feature extraction before pathogen classification. By performing these preparatory actions on the raw signal, the system reduces the complexity of subsequent analysis while maintaining high pathogen identification accuracy through pre-processed acoustic parameters.
Solution Approach 2:
The patent creates simplified representations (copies) of the complex acoustic signal in the form of extracted features such as spectral centroids, formant frequencies, and temporal patterns. These feature copies capture the essential pathogen-specific information while being computationally simpler to analyze than the full raw signal, thus reducing device complexity while maintaining measurement precision.
3Reliability
If continuous monitoring is implemented to improve detection reliability, then productivity is improved, but energy consumption increases
Solution Approach 1:
The patent implements periodic analysis of acoustic parameters at defined intervals rather than continuous full-signal processing. The system monitors continuously for presence detection but performs comprehensive multi-parameter analysis periodically when distress is detected, maintaining high detection reliability while reducing overall energy consumption through intermittent deep analysis.
Solution Approach 2:
The patent applies partial analysis to the continuous signal by focusing computational resources only on segments where respiratory distress is detected. Instead of processing every segment with full complexity, the system performs simplified monitoring continuously and applies detailed multi-parameter analysis only when needed, achieving high reliability with reduced energy consumption through selective intensive processing.
Data Source
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AI summary
The invention relates to a method carried out by a processor for predicting a specific respiratory pathogen in a group of farm animals, which method comprises monitoring the sounds generated by said farm animals.