Hearing Device Self-Fitting via Categorical Perception
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Solution Overview
Problem
Traditional methods for fitting hearing devices rely on average perception data and subjective inputs, leading to inaccurate fittings and high costs, with limited accessibility and acceptance due to the need for direct professional involvement.
Innovation Solution
An automated, real-time speech-based testing system that uses categorical perception to determine individualized hearing device parameter settings, employing signal adjustment and optimization algorithms like constraint-based reasoning, machine learning, and graph theory to tailor the device settings for maximum speech intelligibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods using average perception data and subjective inputs are used, then the fitting process is simple and cost-effective, but the accuracy of hearing device fitting deteriorates
Solution Approach 1:
The system enables automated self-fitting where the hearing device itself performs the fitting process by presenting test signals, collecting user responses, and automatically adjusting parameters based on categorical perception boundaries. This eliminates the need for professional involvement while achieving individualized accurate fitting.
Solution Approach 2:
The system systematically varies signal parameters (frequency, duration, intensity) to map categorical perception boundaries, using these parameter changes to automatically determine optimal hearing device settings tailored to each user's perceptual characteristics.
2Adaptability or versatility
If traditional methods requiring direct professional involvement are used, then fitting accuracy can be maintained, but accessibility and cost-effectiveness deteriorate
Solution Approach 1:
The hearing device performs automated fitting without professional involvement by using categorical perception testing to automatically determine individualized parameters, making the service accessible to anyone with the device while maintaining accuracy through objective perceptual measurements.
Solution Approach 2:
The system replaces the mechanical process of professional assessment and manual adjustment with an automated electronic system that uses signal processing and algorithms to determine fitting parameters based on user responses to test signals.
3Reliability
If subjective inputs from patients are used, then the fitting process is simple to implement, but the reliability of fitting results deteriorates
Solution Approach 1:
The system replaces subjective patient inputs with objective categorical perception measurements through automated signal presentation and analysis, eliminating bias and inconsistency while maintaining ease of use through automated data collection and processing.
Solution Approach 2:
The system uses real-time feedback from user responses to test signals to automatically adjust and refine fitting parameters, creating a closed-loop system that continuously optimizes settings based on actual perceptual data rather than subjective reports.
4Measurement precision
If automated real-time speech-based testing is implemented, then individualized fitting accuracy improves, but device complexity and testing time increase
Solution Approach 1:
The system efficiently maps categorical perception boundaries by strategically varying signal parameters across critical dimensions, using the inherent properties of categorical perception to reduce the number of test points needed while maintaining comprehensive coverage of perceptual space.
Data Source
AI summary
Hearing device configuration and hearing treatment using categorical perception; systems and methods for categorical perception based configuration of hearing devices and hearing treatment.