Facial Device Recommendation Using Anatomical Image-Based Fitting
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Solution Overview
Problem
Existing facial devices, such as CPAP masks, often fail to provide an optimal fit and comfort due to variations in user anatomical dimensions and preferences, leading to suboptimal functionality and user experience.
Innovation Solution
A system and method for adaptively generating facial device recommendations based on visually determined anatomical dimension data using a Bayesian-based scoring metric, incorporating both quantitative and qualitative data, including user feedback and clinical data, to provide personalized device suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If facial devices are designed with fixed sizes and shapes, then manufacturing and distribution are simplified, but they cannot accommodate variations in user anatomical dimensions leading to poor fit and comfort
Solution Approach 1:
The system performs preliminary actions by automatically capturing user facial images and calculating anatomical dimensions before the user needs a facial device. The processor determines nasal-oral region dimensions, face width, and other critical measurements in advance, storing this data to enable rapid, personalized device recommendations without requiring manual measurement or fitting sessions.
Solution Approach 2:
The system incorporates feedback mechanisms where users can provide feedback on device fit and comfort, and clinicians can review recommendations. This feedback loops back into the recommendation model to refine future selections. The Bayesian-based scoring metric continuously updates based on feedback data, improving the accuracy of personalized recommendations over time.
2Manufacturing precision
If personalized facial device recommendations are provided based on anatomical data, then fit and comfort are improved, but the system complexity and computational requirements increase
Solution Approach 1:
The patent replaces manual measurement and fitting processes with automated image processing and computational algorithms. Instead of requiring physical measurements or manual device trials, the system uses computer vision to extract anatomical dimensions from facial images and applies Bayesian probabilistic models to generate recommendations, significantly reducing manual intervention while improving precision.
Solution Approach 2:
The system changes parameters by transforming raw image data into standardized anatomical dimension parameters (nasal-oral region width, face width, nose bridge height, etc.). These standardized parameters are then input into the Bayesian scoring model, which processes multiple variables and their probabilistic relationships to generate optimized device recommendations based on updated parameter values.
3Adaptability or versatility
If multiple facial device options are presented to users, then user preferences and comfort can be optimized, but the decision-making process becomes more time-consuming
Solution Approach 1:
The system applies partial action by providing a curated subset of highly recommended device options rather than presenting all possible devices. The Bayesian scoring model ranks devices based on predicted fit and comfort probabilities, presenting only the top-ranked options that are most likely to satisfy user needs, thereby reducing the number of choices while maintaining high adaptability.
Solution Approach 2:
The system uses feedback from user selections and comfort reports to refine future recommendations. Over time, the model learns individual user preferences and adjusts its scoring, reducing the need for users to review multiple options. The feedback loop enables the system to narrow down recommendations more effectively with each interaction, decreasing selection time.
4Productivity
If automated image-based anatomical measurement is implemented, then measurement speed and consistency are improved, but measurement accuracy may be affected by image quality and processing limitations
Solution Approach 1:
The system incorporates feedback mechanisms where the quality and accuracy of image-based measurements are continuously validated and refined. Clinicians can review and correct automated measurements, and users can provide feedback on device fit that indirectly validates measurement accuracy. This feedback loops back into improving the measurement algorithms and image processing techniques.
Solution Approach 2:
The system handles parameter changes by using multiple imaging parameters and processing techniques to cross-validate measurements. The Bayesian model incorporates uncertainty parameters and weightings that adjust measurement reliability based on image quality, allowing the system to maintain high productivity while compensating for potential inaccuracies through probabilistic reasoning and multiple data sources.
Data Source
AI summary
Devices and methods for adaptively generating facial device selections based on visually determined anatomical dimension data. The device may include a processor and a memory. The memory may include processor-executable instructions for receiving image data representing a user face and determining anatomical dimension data associated with a nasal-oral region of the user face based on the received image data. The processor-executable instructions may be for generating one or more facial device recommendations based on a recommendation model and the anatomical dimension data and providing the one or more facial device recommendations for display at a user interface.