Acoustic Respiratory Threshold Detection via Microphone
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Solution Overview
Problem
Conventional respiratory analysis methods are costly, invasive, and lack accuracy due to the need for specialized equipment and trained personnel, and they do not effectively analyze full breath cycles, including inhale, transition, exhale, and rest phases.
Innovation Solution
A method and apparatus using a microphone to record breathing sounds, process them into audio respiratory signals, and extract metrics for breath intensity and rate, calculating thresholds through peak values in a master vector, allowing for non-invasive and accurate determination of ventilatory and respiratory compensation thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional respiratory analysis methods are used, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex mechanical respiratory analysis equipment with an acoustic-based system using a microphone to capture breath sounds. The system processes audio signals to extract respiratory metrics and detect thresholds, substituting mechanical/gas analysis systems with an acoustic field-based approach that is simpler and more accessible.
Solution Approach 2:
The patent creates a computational model that replicates the functionality of complex respiratory analysis systems. By processing audio signals through signal processing algorithms and machine learning models, the system copies the threshold detection capability of expensive equipment while using readily available microphones and standard computing resources.
2Measurement precision
If conventional respiratory analysis methods are used, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs automated threshold detection without requiring trained personnel to conduct the test. The audio processing algorithms automatically analyze breath sounds, extract respiratory metrics, and identify thresholds, making the system self-sufficient and eliminating the need for specialized operator knowledge.
Solution Approach 2:
The patent replaces manual, operator-dependent respiratory analysis with an automated acoustic system. The microphone-based approach combined with automatic signal processing eliminates the need for trained personnel to perform complex gas analysis procedures, significantly improving ease of operation.
3Measurement precision
If conventional respiratory analysis methods are used, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system continuously processes breath sounds in real-time, analyzing each breath cycle as it occurs. This continuous analysis allows for dynamic threshold detection without requiring discrete, time-consuming measurement intervals, reducing overall test duration while maintaining accuracy.
Solution Approach 2:
The patent analyzes respiratory patterns at regular intervals during breath cycles, extracting metrics from periodic breath sounds. This periodic analysis approach enables efficient threshold detection by sampling at optimal moments in the breathing cycle, reducing total measurement time compared to continuous complex analysis.
4Reliability
If conventional respiratory analysis methods are used, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical respiratory analysis equipment with an acoustic system that uses a simple microphone and computational algorithms. This substitution maintains reliability through robust signal processing and pattern recognition while dramatically reducing device complexity and eliminating the need for specialized gas analysis equipment.
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
Enables cost-effective, non-invasive, and accurate detection of ventilatory and respiratory compensation thresholds, analyzing full breath cycles and providing insights into respiratory health without the need for expensive equipment or trained personnel.
Implementation Method 1
A plurality of microphones 220 may be used to capture the breath sounds of the user.
Data Source
AI summary
A method for detecting thresholds in a breathing session is disclosed. The method comprises recording breathing sounds of a subject using a microphone. The method further comprises processing the breathing sounds to generate an audio respiratory signal and recognizing a plurality of breath cycles from the audio respiratory signal. Additionally, the method comprises extracting metrics related to a breath intensity and a breath rate from the plurality of breath cycles and producing a plurality of vectors using the metrics related to the breath intensity and the breath rate. Further, the method comprises calculating a master vector by summing the plurality of vectors and assigning each value in the master vector with a weighting coefficient and determining the thresholds using peak values in said master vector.