Hearing Device Health Issue Detection via Activity Pattern Analysis
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Solution Overview
Problem
Current hearing devices and systems lack an effective method for early detection of health issues, such as cognitive decline or dementia, which can lead to delayed intervention and reduced treatment success.
Innovation Solution
A method and computer program implemented in hearing devices that utilize sound detectors, user detectors, and processing units to analyze audio and user activity patterns over time, employing machine learning algorithms like neuronal networks to identify deviations from healthy patterns and provide early detection signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hearing devices collect and analyze user activity data over time to detect health issues, then detection accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the health detection system into multiple independent components: a hearing device that collects audio and activity data, a user device that receives and stores data, and a processing system that performs machine learning analysis. This segmentation allows the complex detection algorithms to run on external devices while keeping the hearing device itself relatively simple.
Solution Approach 2:
The patent introduces a user device (smartphone, tablet, or server) as an intermediary between the hearing device and the health detection system. This intermediary handles the complex data processing and machine learning operations, enabling accurate health issue detection without requiring the hearing device itself to be overly complex.
2Reliability
If hearing devices process large amounts of audio and sensor data continuously, then detection reliability improves, but energy consumption increases
Solution Approach 1:
The patent extracts the computationally intensive data processing and machine learning operations from the hearing device and relocates them to external user devices or servers. This allows the hearing device to continuously collect data with minimal energy consumption while maintaining high detection reliability through comprehensive data analysis performed externally.
Solution Approach 2:
The system implements periodic data collection and transmission, where the hearing device gathers audio and activity data over time intervals and transmits batches of data to the user device for processing. This periodic approach reduces continuous processing demands and energy consumption while maintaining reliable detection through accumulated data analysis.
3Loss of time
If the system monitors user activity patterns over extended periods, then early detection capability improves, but data storage requirements increase
Solution Approach 1:
The system performs preliminary local processing and filtering of data at the hearing device and user device levels before storing or transmitting to long-term storage. This preliminary action reduces the volume of raw data that needs to be stored while preserving the essential information needed for early health issue detection through pattern recognition.
Data Source
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AI summary
A method for early detection of a health issue of a user wearing a hearing device (12) is provided. The hearing device (12) comprises at least one sound detector (20) for generating an audio signal, at least one user detector for generating a user detection signal, a processing unit (23) for modifying the audio signal, and at least one sound output component (24) for outputting the modified audio signal. The method comprises: receiving (S2) the audio signal and/or the user detection signal for a predetermined time interval; determining (S4) a current activity pattern of the user by evaluating the audio signal and/or the user detection signal received over the predetermined time interval; evaluating (S6) the current activity pattern; determining (S8) whether there is an upcoming health issue of the user depending on the evaluation; and providing (S10), if it is determined that there is an upcoming health issue of the user, an output signal indicative of the health condition.