Hearing Aid Simulation Model for Automated Parameter Optimization
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Solution Overview
Problem
Current hearing aid personalization methods are limited by the availability of audiologists, requiring cumbersome and inefficient manual adjustments, and fail to explore the full parameter space of settings due to the inability to test all combinations in various sound environments and user intents.
Innovation Solution
A hearing system utilizing a simulation model and AI to iteratively optimize hearing aid settings based on user data, including sound environments, user intents, and personality traits, allowing for automated and continuous personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment by audiologist is used, then personalized hearing aid settings can be achieved, but the process becomes cumbersome and inefficient due to limited audiologist availability
Solution Approach 1:
The system enables self-service by allowing the hearing aid to automatically adjust its own parameters based on real-time environmental sensing and user feedback. The device performs self-diagnosis and self-optimization without requiring continuous audiologist intervention, thus eliminating scheduling delays and making the personalization process continuously available.
Solution Approach 2:
The system performs preliminary actions by pre-configuring multiple parameter sets for different environmental conditions before the user encounters them. When the hearing aid detects a specific environment, it can immediately apply the pre-prepared optimal settings, avoiding the need for real-time manual adjustment and reducing the time loss associated with iterative fitting sessions.
2Adaptability or versatility
If comprehensive parameter space exploration is attempted, then optimal settings for all sound environments can be found, but the complexity of testing all combinations becomes unmanageable without continuous audiologist availability
Solution Approach 1:
The system segments the vast parameter space into manageable subsets, each optimized for specific sound environments or user activities. Instead of exploring all possible combinations simultaneously, the hearing aid divides parameters into environment-specific groups (e.g., noisy environments, quiet environments, speech-focused, music-focused) and applies appropriate segments based on real-time detection, thus achieving comprehensive coverage without unmanageable complexity.
Solution Approach 2:
The system employs dynamic parameter adjustment where the hearing aid continuously adapts settings based on real-time environmental sensing and user feedback. Rather than statically exploring all parameter combinations, the device dynamically selects and adjusts parameters according to current conditions, making the complexity manageable through adaptive, context-dependent optimization.
3Reliability
If scheduled fitting sessions are used for parameter updates, then professional oversight is maintained, but the frequency and timeliness of optimizations are limited
Solution Approach 1:
The system maintains continuous optimization by constantly monitoring environmental conditions and user responses, automatically adjusting parameters in real-time without interruption. This continuous useful action ensures that the hearing aid remains optimized at all times, eliminating the gaps between scheduled fitting sessions while maintaining professional oversight through periodic remote reviews of the collected data.
Solution Approach 2:
The system implements continuous feedback loops where user responses (explicit ratings or implicit physiological signals) are immediately processed to adjust parameters. This real-time feedback mechanism maintains reliability by ensuring settings remain accurate and appropriate, while dramatically increasing productivity by allowing numerous optimizations between traditional fitting sessions without requiring direct audiologist involvement for each adjustment.
Data Source
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AI summary
A hearing system comprises a processing device, a hearing aid adapted to be worn by a user, and a data logger. The hearing aid comprises an input transducer providing an electric input signal representing sound in the environment of the user, and a hearing aid processor executing a processing algorithm in dependence of a specific parameter setting. The data logger stores time segments of said electric input signal, and data representing a corresponding user intent. The processing device comprises a simulation model of the hearing aid. The simulation model is based on a learning algorithm configured to provide a specific parameter setting optimized to the user's needs in dependence of a hearing profile of the user, the logged data, and a cost function. A method of determining a parameter setting for a hearing aid is further disclosed.