Mobile App Mask Sizing via Facial Feature Measurement
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Solution Overview
Problem
Current respiratory disorder treatment systems face challenges in patient compliance due to uncomfortable, poorly fitting, and aesthetically unappealing patient interfaces for positive pressure therapies, which are often labor-intensive to size and require professional assistance.
Innovation Solution
An automated system for patient interface sizing using image capture technology that measures facial features to recommend the appropriate size based on a comparison with standard sizes, allowing for convenient and accurate selection without trained assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional professional sizing assistance is used, then measurement precision is improved, but device complexity and ease of operation worsen due to requiring trained personnel and labor-intensive processes
Solution Approach 1:
The system enables patients to perform self-sizing using a mobile device camera to capture facial images, with automated image processing and measurement algorithms calculating facial features without requiring trained personnel. The mobile application guides patients through the process and provides mask size recommendations based on automated analysis of captured images.
Solution Approach 2:
The patent replaces manual mechanical measurement tools (rulers, calipers) and professional expertise with an automated digital image processing system. The mobile device camera captures facial images, and software algorithms automatically detect facial landmarks, calculate distances between features, and determine mask size, eliminating the need for physical measurement tools and trained operators.
2Ease of operation
If automated sizing is implemented, then ease of operation is improved, but measurement precision may worsen without professional assistance
Solution Approach 1:
The system provides real-time feedback during image capture by displaying the captured image on the mobile device screen and allowing patients to retake images if unsatisfied. The automated measurement process includes validation steps to ensure image quality meets minimum standards before processing, and the system can prompt users to adjust positioning or lighting to improve measurement accuracy.
Solution Approach 2:
The mobile application guides patients through preliminary actions before measurement, including positioning instructions, lighting requirements, and capture protocols. The system pre-processes images to enhance facial feature detection and applies correction algorithms to compensate for common positioning errors, ensuring accurate measurements even with user-captured images.
3Measurement precision
If professional sizing assistance is required, then measurement precision is improved, but productivity worsens due to time-consuming processes
Solution Approach 1:
Patients can perform mask sizing independently at home using their own mobile devices, eliminating the need to schedule appointments with healthcare providers or visit facilities for sizing. The automated system processes images instantly and provides immediate mask size recommendations, enabling patients to order appropriate masks without delays associated with professional scheduling and in-person measurements.
Data Source
AI summary
Apparatus and methods automate selection of patient interface(s). Image data captured by an image sensor may contain facial feature(s) of a user. The facial features may be captured in association with a predetermined reference feature of known dimension(s) such as with a user interface display that is generated with a sequence of icons that are activated for directing and tracking movement within the interface for desired image capture. The user's facial feature(s) and the reference feature may be detected in the captured image data. The image may be processed to measure an aspect of the detected facial feature(s) based on the reference feature. A patient interface size may be detected from standard patient interface sizes based on a comparison between the measured aspect of the facial feature(s) and a data record relating sizing information of the standard patient interface sizes and the measured aspect of the facial feature(s).