Genetic Algorithm Hearing Aid Fitting
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Solution Overview
Problem
Current methods for fitting hearing assistance devices are impractical and inefficient, as they often require extensive time and resources, and fail to account for individual user preferences due to their reliance on general prescriptive formulas and impractical paired comparison strategies.
Innovation Solution
The use of genetic algorithms that incorporate subjective user input through crossover and mutation operations, where child sets are generated by arithmetic or geometrical operations on parent sets, and mutation involves replacing lowest ranked parameter values with randomly generated ones, to optimize device settings for individual users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If paired comparisons are used to determine optimal hearing aid settings through iterative tournaments, then individual user preferences can be identified, but the process becomes extremely impractical in terms of time and financial resources
Solution Approach 1:
The patent applies preliminary action by pre-generating multiple candidate hearing aid configurations (parent sets) with varied parameters before the actual fitting process. This allows the system to present only the most relevant options to the user for comparison, rather than requiring exhaustive paired comparisons of all possible settings combinations.
Solution Approach 2:
The patent uses copying by creating child sets through crossover operations that replicate and recombine parameters from parent sets. This generates new candidate configurations based on successful parent configurations, reducing the need to evaluate all possible settings from scratch and thereby reducing the time and resources required for the fitting process.
2Productivity
If prescriptive fitting formulas are used to adjust hearing aid settings for large numbers of users, then the fitting process becomes more efficient, but individual user preferences are ignored
Solution Approach 1:
The patent applies local quality by allowing different regions of the parameter space to have different levels of individualization. Common parameters can be set using prescriptive formulas for efficiency, while specific parameters that strongly influence individual user preference can be customized through the genetic algorithm and user feedback, achieving both efficiency and adaptability.
Solution Approach 2:
The patent uses dynamics by making the fitting process adaptive and iterative rather than static. The system starts with prescriptive formulas for efficiency, then dynamically adjusts parameters based on user feedback through the genetic algorithm, allowing the fitting to evolve and adapt to individual preferences while maintaining overall process efficiency.
3Adaptability or versatility
If genetic algorithms are used to optimize hearing aid settings with user input, then individual preferences are captured, but the complexity of the algorithm increases
Solution Approach 1:
The patent applies parameter changes by representing hearing aid settings as configurable parameters within the genetic algorithm. The system manages complexity by focusing the genetic algorithm optimization on the most critical parameters that influence user preference, rather than attempting to optimize all possible parameters simultaneously, thereby balancing adaptability with manageable complexity.
Data Source
AI summary
Disclosed herein, among other things, is an apparatus for fitting a hearing assistance device using a genetic algorithm. The apparatus includes a first population of a plurality of parent sets representing at least one device parameter. A first pair from the parent sets is presented with assistance of the hearing assistance device, the first pair comprising a first and second set. A user selects a preference between the first and second sets. A child set is determined by operating on at least one set of the plurality of parent sets. The child set can include a crossover of the at least one parent set, where the crossover includes an arithmetic or geometrical operation to parameter values of the parent set, or a mutation of the at least one parent set, where the mutation includes replacing a lowest ranked parameter value in the parent set with a randomly generated parameter value.