Frontal Accelerometer Cough Detection With Lower Processing Load
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Solution Overview
Problem
Existing medical devices struggle to accurately detect patient coughs using accelerometer signals while minimizing computational resources and device usage.
Innovation Solution
A medical device system that utilizes a frontal accelerometer signal analysis to identify cough patterns, characterized by a smooth increase, sharp decrease, peak, and gradual return to baseline, allowing for efficient cough detection with reduced computing power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full accelerometer signal analysis is used to detect coughs, then detection accuracy is improved, but computational resources and device complexity increase
Solution Approach 1:
The patent segments the accelerometer signal into three orthogonal components (frontal, lateral, vertical) and identifies that only the frontal component contains relevant cough information. This segmentation allows the system to analyze only the necessary portion of the signal, reducing computational load while maintaining detection accuracy.
Solution Approach 2:
The patent extracts and isolates the frontal component of the accelerometer signal for cough detection, removing unnecessary lateral and vertical components from the analysis. This extraction process reduces the data volume requiring processing while preserving the essential cough detection capability.
2Reliability
If multiple accelerometer components are analyzed for cough detection, then detection reliability is improved, but computational resources increase
Solution Approach 1:
The patent divides the three-axis accelerometer signal into separate components and identifies that cough detection relies specifically on the frontal component. This segmentation enables the system to process only one component instead of all three, reducing computational resource consumption while maintaining reliable cough detection.
3Measurement precision
If comprehensive cough analysis is performed, then patient condition monitoring accuracy is improved, but processing time increases
Solution Approach 1:
The patent extracts only the essential frontal component features relevant to cough detection, excluding unnecessary analysis of other components. This focused extraction approach reduces processing time while maintaining accurate patient condition monitoring by concentrating computational effort on the most diagnostically relevant signal features.
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
Accurately detects coughs with less computational resources and fewer devices, enabling monitoring of cough frequency and severity for conditions like COPD, facilitating timely medical intervention.
Implementation Method 1
an accelerometer configured to collect an accelerometer signal, wherein the accelerometer signal is indicative of one or more patient movements that occur during a cough
Data Source
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AI summary
This disclosure is directed to techniques for recording and recognizing physiological parameter patterns associated with symptoms. A medical device system includes a medical device including an accelerometer configured to collect an accelerometer signal that indicates one or more patient movements that occur during a cough. Additionally, the medical device system includes processing circuitry configured to: determine whether the accelerometer signal satisfies a set of criteria corresponding to a cough pattern comprising a smooth increase from a baseline, then a sharp decrease, a peak within the sharp decrease, then a gradual return to the baseline; and identify a cough based on the determination that the accelerometer signal satisfies the set of criteria.