Acoustic Mask Identification via Flow Path Features
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Solution Overview
Problem
Current CPAP devices lack an economical and accurate method to automatically identify connected masks, leading to incorrect setting configurations and decreased patient compliance due to the need for manual input and lack of compatibility with various mask models.
Innovation Solution
The implementation of an acoustic detection system using a single microphone to characterize connected masks based on reflected sound waves, incorporating unique acoustical features in the mask's flow path, such as material composition and geometry, to improve mask identification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual input method is used to configure therapy settings, then device complexity is reduced, but measurement precision of mask identification deteriorates
Solution Approach 1:
The patent replaces the manual mechanical input system with an acoustic detection system. A microphone captures acoustic signals from the mask's flow path, and signal processing algorithms automatically identify the mask model and configure therapy settings, eliminating the need for manual configuration while improving identification accuracy.
Solution Approach 2:
The mask identification system performs self-service by automatically detecting the mask model through acoustic signals and configuring appropriate therapy settings without requiring user intervention. The system independently completes the identification and configuration tasks that previously required manual input.
2Measurement precision
If acoustic detection system is implemented, then mask identification accuracy is improved, but device complexity increases
Solution Approach 1:
The acoustic detection system serves multiple functions: it identifies mask models, determines flow path characteristics, and automatically configures therapy settings. By consolidating these functions into a single integrated system, the patent reduces the need for multiple separate components that would otherwise be required.
Solution Approach 2:
The patent introduces signal processing algorithms as an intermediary between the acoustic sensor and the therapy control system. These algorithms process the raw acoustic signals to extract mask identification information and automatically determine appropriate therapy settings, bridging the gap between detection and control without requiring direct complex hardware integration.
3Ease of operation
If automatic mask identification is implemented, then patient compliance is improved, but loss of time in system setup increases
Solution Approach 1:
The acoustic detection system performs mask identification and therapy configuration automatically during the initial system setup, before the patient begins therapy. By completing these preparatory actions in advance, the system eliminates the need for repeated manual configuration during subsequent use, improving compliance without adding ongoing setup time.
4Adaptability or versatility
If multiple mask models are supported, then adaptability is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent incorporates unique acoustic features at specific locations within the flow path of each mask model. These localized acoustic characteristics serve as distinct identifiers that enable the detection system to differentiate between multiple mask models by analyzing the acoustic signals from these specific locations.
Solution Approach 2:
The detection system analyzes changes in acoustic signal parameters such as frequency, amplitude, and waveform characteristics to distinguish between different mask models. By monitoring these parameter variations, the system can accurately identify and adapt to multiple mask models without increasing overall system complexity.
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
This solution enables accurate, automatic, and cost-effective mask identification, improving patient compliance by ensuring correct therapy settings and informing future mask design, while reducing the complexity of setting configurations.
Implementation Method 1
characterize connected masks based on reflected sound waves
Implementation Method 2
acoustic detection system using a single microphone to characterize connected masks
Data Source
AI summary
Respiratory patient interface (e.g., mask) structures may be modified to include acoustical feature(s) (AF) to provide differences in structure that can produce, when acoustically sensed, a unique acoustic signature for identification of a particular model mask and thereby differentiate it from other model masks. The added AF may be generally similar across different models of patient interface but have detectable differences between the models. However, the added AF of a particular model are typically substantially the same for all masks of that particular model. Such AF may, for example, be a change in material composition of a section or part of a flow path of the patient interface, a change in a dimension, such as a length, of a section or part of a flow path of the patient interface, and/or an expansion and/or contraction of a section (e.g., diameter) or part of a flow path of patient interface.