Hearing Device Social Interaction Metric via Sensor Fusion
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Solution Overview
Problem
Current methods for assessing social interaction in hearing-impaired individuals are limited, as they rely on self-reported data and do not provide comprehensive, automatic tracking of social engagement, which can lead to underestimation of the impact of hearing devices on social activity.
Innovation Solution
A method and system using a hearing device with integrated microphones and sensors, along with classifiers, to automatically determine social interaction metrics by analyzing audio and sensor signals, categorizing user activities, and assigning weights to define social interaction levels, thereby providing a comprehensive assessment of social engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If self-reported questionnaires are used to assess social interaction, then data collection is simple and low-cost, but measurement precision and reliability are insufficient due to underestimation of hearing device impact
Solution Approach 1:
The patent segments social interaction assessment into multiple dimensions: audio signal analysis (speech detection, conversational turn-taking), sensor data (physical activity, proximity), and contextual information (time, location). Each dimension is measured separately by dedicated components within the hearing device, then integrated to form a comprehensive social interaction profile, thereby improving measurement precision without overwhelming system complexity
Solution Approach 2:
The hearing device is designed to perform multiple functions: primary hearing assistance plus social interaction monitoring. The same microphones and sensors used for hearing enhancement are repurposed to detect speech, analyze conversational patterns, and track physical activity related to social engagement. This multi-functionality approach improves measurement accuracy without requiring entirely separate assessment systems
2Extent of automation
If comprehensive sensor integration and automatic tracking are implemented, then social interaction tracking becomes accurate and automated, but device complexity and power consumption increase
Solution Approach 1:
The patent merges social interaction monitoring functions with the core hearing device architecture. Microphones, sensors, and processors that are essential for hearing assistance are combined with social interaction analysis capabilities. The classifier module integrates multiple data streams (audio, motion, proximity) into unified social interaction metrics, achieving high automation while managing complexity through functional integration rather than separate systems
Solution Approach 2:
The hearing device automatically performs social interaction tracking without requiring external monitoring equipment or manual intervention. The device self-monitors its own operational context, using its embedded sensors and classifiers to continuously assess social engagement levels, conversational participation, and activity patterns. This self-service capability achieves comprehensive automation while minimizing additional system complexity
3Measurement precision
If detailed audio and sensor signal analysis is performed, then social interaction metrics become more precise, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary classification of audio and sensor signals to identify socially relevant events before detailed analysis. The system first detects potential social interactions (speech detection, proximity events), then applies more computationally intensive analysis only to these identified events. This staged approach maintains high measurement precision for social interaction metrics while reducing overall processing time by avoiding continuous full-scale analysis of all signals
Data Source
AI summary
A method for determining social interaction of a user wearing a hearing device which comprises at least one microphone and at least one classifier. The method comprises: receiving an audio signal from the at least one microphone and/or a sensor signal from the at least one further sensor; identifying, by the at least one classifier, one or more predetermined user activity values by evaluating the audio signal from the at least one microphone and/or the sensor signal from the at least one further sensor; and calculating a user social interaction metric indicative of the social interaction of the user from the identified user activity values, wherein the user activity values are assigned to predefined social interaction levels, and wherein the user social interaction metric is a function of the user activity values weighted with their respective contribution to each of the social interaction levels.