Hearing Aid Personalization via Machine Learning
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Solution Overview
Problem
Conventional hearing aid devices are not adequately personalized to meet the unique auditory needs and preferences of users in varying environments, as they are typically optimized for sound-proof environments and do not account for changes in user activity or environmental acoustics.
Innovation Solution
The use of machine-learning technology to personalize and fine-tune the parameters of hearing aid devices based on environment characteristics and user preferences, by training a machine-learning model with sensor data and user settings to adapt the device settings in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hearing aid devices are optimized for sound-proof environments, then they provide reliable performance in controlled conditions, but they fail to adapt to varying environmental acoustics and user activities
Solution Approach 1:
The patent implements dynamic adaptation by training a machine-learning model with sensor data collected across multiple environments and user activities. The model learns to dynamically adjust hearing aid parameters based on current environmental conditions and user behavior patterns, transforming the device from a static configuration to an adaptive system that evolves with usage
Solution Approach 2:
The patent applies preliminary action by collecting and storing sensor data and user settings during a training phase before actual use. The machine-learning model is pre-trained with this data to anticipate and adapt to different environments and activities, enabling the device to provide reliable performance across varying conditions without requiring real-time manual adjustment
2Adaptability or versatility
If hearing aid parameters are manually adjusted for different environments, then user preferences are accommodated, but the process requires significant user intervention and time
Solution Approach 1:
The patent implements self-service by enabling the hearing aid to automatically adjust parameters based on environment characteristics and user behavior patterns learned during training. The device monitors sensor data in real-time and autonomously modifies settings without requiring user intervention, eliminating the time loss associated with manual adjustments while maintaining high adaptability
Solution Approach 2:
The patent incorporates feedback mechanisms where the machine-learning model continuously monitors sensor data and user interactions, then adjusts parameters based on this feedback loop. The system learns from user responses to environmental changes and refines its parameter selection over time, achieving both customization and time efficiency
3Measurement precision
If hearing aid devices collect and process extensive sensor data for personalization, then accurate user preferences are captured, but device complexity and processing requirements increase
Solution Approach 1:
The patent introduces an intermediary machine-learning model that processes sensor data and user settings to determine optimal parameters. This intermediary layer simplifies the complexity by encapsulating the complex processing logic within a trained model, allowing the hearing aid to achieve high measurement precision for user preferences without exposing the underlying system complexity to the user or requiring complex real-time processing
Data Source
AI summary
Training data are obtained. Each training datum includes environment characteristics obtained based on sensor data. Respective user settings corresponding to the training data are obtained. At least one respective user setting corresponds to one training datum, and a respective user setting is indicative of a user preference of at least one parameter of a hearing aid device. A machine-learning model for the hearing aid device is trained to output values for the at least one parameter. The hearing aid device is reconfigured based on an output of the machine-learning model. Reconfiguring the hearing aid device includes using current environment characteristics as an input to the machine-learning model to obtain at least one current value for the at least one parameter and configuring the hearing aid device to use at least one current value.