Cough Analysis System for Respiratory Illness Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting severe respiratory illnesses through cough analysis rely on subjective clinical judgment, which is unreliable and fails to identify infections early enough to prevent transmission, especially in remote clinical settings.
Innovation Solution
A system that captures and analyzes acoustic data from coughs using a microphone to identify specific acoustic properties, determining the presence of severe respiratory illnesses or abnormal pulmonary systems through a cough classification algorithm and database comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective clinical judgment is used to assess cough characteristics, then clinical assessment can be performed without additional equipment, but the reliability and objectivity of the assessment deteriorates
Solution Approach 1:
The patent replaces the mechanical/subjective human judgment system with an acoustic analysis system that uses microphones, signal processing, and computational algorithms to objectively measure cough characteristics. This substitution transforms the assessment from subjective perception to objective quantitative analysis, resolving the contradiction between ease of operation and reliability.
2Measurement precision
If acoustic analysis is implemented to objectively quantify cough characteristics, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The system performs self-calibration and automatic assessment without requiring external clinical intervention for each measurement. The acoustic analysis algorithm automatically processes cough signals, extracts features, and generates assessments, reducing the need for complex manual calibration procedures and specialized equipment setup.
Solution Approach 2:
The patent transforms complex acoustic signals into simplified quantitative parameters through signal processing. By converting raw audio data into extracted acoustic features and summary metrics, the system maintains measurement precision while reducing the complexity of data interpretation and clinical workflow.
3Reliability
If early identification of severe respiratory illnesses is achieved through automated cough assessment, then transmission prevention improves, but the complexity of detection and measurement increases
Solution Approach 1:
The patent replaces complex manual clinical assessment procedures with automated acoustic analysis. The system uses computational algorithms to detect subtle cough characteristics that indicate severe respiratory illnesses, making the detection process more accurate but initially more complex. However, this automation ultimately simplifies the workflow by eliminating manual judgment variability.
Solution Approach 2:
The system provides immediate automated feedback on cough assessment results, enabling real-time identification of severe respiratory illnesses. This feedback mechanism allows for prompt intervention and transmission prevention, addressing the complexity by creating a closed-loop system that continuously monitors and alerts clinicians.
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
Provides objective and timely identification of severe respiratory illnesses, reducing transmission risks by automating the assessment of coughs and improving clinical diagnosis in remote locations.
Implementation Method 1
using a microphone to receive live acoustic data representing a cough train of the human subject
Data Source
AI summary
A computer-implemented method comprises: (A) receiving first acoustic data representing a first cough train of a first human subject, wherein the first cough train comprises at least one first cough of the first human subject; (B) identifying at least one first value of at one first acoustic property of the first acoustic data; and (C) determining, based on the at least one first value of the at least one first acoustic property, whether the first acoustic data indicates that the first human subject has a severe respiratory illness.