Respiratory Therapy Device Mask Characterization via Airflow Analysis
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Solution Overview
Problem
Users of respiratory therapy systems face challenges in selecting the appropriate mask type or size, achieving a good mask seal, and perceiving the benefits of the therapy due to discomfort, complexity, and aesthetic issues, leading to reluctance in using the systems effectively.
Innovation Solution
A method and system that automatically characterize the user interface by generating airflow with and without the mask, using airflow parameter data to predict the mask type and size, and adjust the therapy device settings for improved comfort and efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automatic mask characterization is implemented using airflow parameter data, then therapy setup time and accuracy are improved, but device complexity increases
Solution Approach 1:
The respiratory therapy device automatically characterizes the mask by itself using its own motor and airflow sensors, without requiring external equipment or manual intervention. The device performs self-diagnosis by analyzing airflow parameters generated during normal operation to determine mask type, size, and seal quality.
Solution Approach 2:
The patent replaces manual mask selection and fitting procedures with an automated electronic characterization system. Instead of requiring users to manually select mask sizes or for clinicians to physically assess mask fit, the system uses motor control and airflow parameter analysis to automatically identify mask characteristics.
2Measurement precision
If multiple airflow measurements are taken to characterize the user interface, then characterization accuracy is improved, but treatment time is increased
Solution Approach 1:
The system performs mask characterization measurements during the initial setup phase before actual therapy begins. By conducting airflow parameter measurements and motor response tests during this preliminary period, the system establishes accurate mask characteristics without delaying the therapeutic treatment itself.
Solution Approach 2:
The characterization process uses periodic airflow measurements taken at different motor speeds and phases. The motor operates at various speeds during characterization to generate multiple airflow parameter data points, which are then analyzed together to determine mask characteristics with high accuracy.
3Reliability
If the system automatically adjusts therapy settings based on mask characterization, then therapy efficacy is improved, but control system complexity increases
Solution Approach 1:
The system uses feedback from airflow parameter measurements to automatically adjust therapy settings. The motor controller receives feedback about mask characteristics and seal quality, then adjusts motor speed and airflow parameters to optimize therapy delivery for the specific mask configuration and user needs.
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
Enhances therapy efficacy and user experience by accurately identifying the user interface components, ensuring proper settings, and simplifying the setup process, thereby improving the quality of therapy and comfort.
Implementation Method 1
a first airflow to be generated from the respiratory therapy device to the user interface by operating a motor of the respiratory therapy device
Data Source
AI summary
A method includes generating a first airflow that travels from the respiratory therapy device to a user interface by operating a motor of the respiratory therapy device. The method also includes receiving first airflow parameter data associated with the first airflow. The method also includes determining a first user interface prediction, based on the first airflow parameter data. The method also includes generating a second airflow from the respiratory therapy device to the user interface by operating the motor of the respiratory therapy device. The method also includes receiving second airflow parameter data associated with the second airflow. The method also includes determining, based on the second airflow parameter data, a second user interface prediction. The method also includes characterizing the user interface based at least in part on the first user interface prediction and the second user interface prediction.