Hearing Instrument Context Sensing for Automatic Auditory Intent
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users of hearing instruments often need to manually adjust settings for different auditory contexts, which can be inconvenient, embarrassing, or result in suboptimal configurations.
Innovation Solution
A processing system determines the user's auditory intent based on context information using machine learning, automatically adjusting hearing instrument settings to match the intent without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of hearing instrument settings is implemented, then users can control settings for different auditory contexts, but it causes inconvenience, embarrassment, and suboptimal configurations
Solution Approach 1:
The hearing instrument automatically detects auditory context and adjusts settings without user intervention. The processor analyzes sensor data to determine the user's auditory intent and autonomously configures the hearing instrument parameters, eliminating the need for manual adjustment while maintaining adaptability to different contexts.
Solution Approach 2:
The system proactively adjusts hearing instrument settings before the user needs them by continuously monitoring context information and predicting auditory intent. This preliminary action ensures optimal configuration is already in place when the user encounters different auditory environments, avoiding the need for reactive manual adjustments.
2Reliability
If automatic auditory intent determination is implemented using machine learning, then hearing instrument settings are optimized for user's intent, but it increases device complexity
Solution Approach 1:
The processor performs multiple functions including sensor data collection, context information analysis, auditory intent determination, and setting adjustment using a single integrated machine learning model. This multi-functional approach optimizes configuration accuracy while minimizing the addition of separate complex subsystems.
Solution Approach 2:
Context information serves as an intermediary that bridges raw sensor data and auditory intent determination. The processor analyzes intermediate context features (acoustic environment, activity state, location, time) to simplify the machine learning process and reduce computational complexity while maintaining reliable setting configuration.
3Measurement precision
If multiple sensors and context information processing are used, then auditory intent determination accuracy is improved, but it increases energy consumption
Solution Approach 1:
The processor selectively processes context information based on current auditory context and user needs. Not all sensor data is processed continuously at full resolution; instead, the system adjusts processing intensity and data sampling rates dynamically, maintaining sufficient measurement precision while reducing overall energy consumption during different operational states.
Data Source
AI summary
One or more processing circuits may obtain context information associated with a user of one or more hearing instruments, wherein the context information is based on a first set of sensor data generated by a plurality of sensors of the one or more hearing instruments and a second set of sensor data generated by a plurality of sensors of a computing device communicatively coupled to the one or more hearing instruments. The one or more processing circuits may determine, based on at least a portion of the context information, an auditory intent of the user for a given auditory context. The one or more processing circuits may associate the auditory intent with one or more actions, such as actions to adjust one or more settings of the one or more hearing instruments. The one or more processing circuits may invoke the one or more actions associated with the auditory intent.