Spirometer and Activity Sensor Fusion for COPD Exacerbation Prediction
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Solution Overview
Problem
Current devices are unable to predict the onset of exacerbations in lung diseases, such as COPD, as the changes in lung function parameters only occur immediately before or during an exacerbation, making it challenging to initiate timely countermeasures.
Innovation Solution
A device comprising a spirometer unit, an activity measurement unit, and an evaluation unit that collects and combines data on pulmonary respiration and motor movement activity to provide a prognosis for exacerbation occurrence by analyzing changes in lung function and physical activity parameters over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only spirometer unit data is used for exacerbation detection, then the device complexity is low, but the prediction reliability is insufficient because lung function parameters only change immediately before or during exacerbation
Solution Approach 1:
The patent combines data from the spirometer unit (lung function parameters) with data from the activity measurement unit (motor movement activity) to create a composite indicator for exacerbation prediction. This merging of multiple data sources improves prediction reliability by capturing both respiratory changes and behavioral changes that occur during exacerbations, while the evaluation unit integrates these data streams through systematic processing and correlation analysis.
Solution Approach 2:
The evaluation unit serves multiple functions: it processes spirometer data, processes activity measurement data, correlates these different data types, and generates composite indicators. This multi-functionality allows the system to improve prediction reliability without proportionally increasing device complexity, as a single evaluation unit handles diverse analytical tasks.
2Measurement precision
If multiple parameters are monitored continuously to improve prediction accuracy, then the measurement precision improves, but the loss of time for data processing increases
Solution Approach 1:
The evaluation unit continuously processes and pre-analyzes data from both the spirometer unit and activity measurement unit in the background, even before an exacerbation is suspected. This preliminary processing prepares the data for rapid exacerbation detection, so that when changes occur, the system can quickly identify them without experiencing processing delays. The system maintains ready-state analytical capabilities that reduce response time.
Solution Approach 2:
The system implements continuous feedback loops where the evaluation unit constantly monitors incoming data from both measurement units, compares current values against historical patterns and thresholds, and adjusts its analysis in real-time. This feedback mechanism enables the system to process multiple parameters efficiently by learning from ongoing data streams rather than performing exhaustive batch processing, thereby reducing time loss while maintaining high measurement precision.
Data Source
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AI summary
The device (1) comprises a spirometer unit (2) for measuring the lung respiration of a subject, where an activity measuring unit (3) is provided for measuring a motor movement activity of the subject. An evaluation unit (4) is provided for collecting a measuring data of the spirometer unit and the activity measuring unit. An independent claim is also included for a method for detecting exacerbation relevant parameters from a spirometer unit for measuring the lung function of a subject.