Hearing Instrument Sensor System for Insertion Position Assessment
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Solution Overview
Problem
Users of hearing instruments, particularly over-the-counter and direct-to-consumer devices, face challenges in correctly placing in-ear assemblies, leading to discomfort, poor sound quality, and retention issues due to improper wear, which can result in overestimated hearing thresholds and increased power consumption.
Innovation Solution
A processing system utilizing a machine learned (ML) model to determine the correct fitting category of a hearing instrument based on sensor data from various sensors, including IMUs, temperature sensors, and cameras, providing real-time feedback to ensure proper wear and alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users wear hearing instruments without guidance, then the device complexity is reduced, but the fitting accuracy and user comfort deteriorate
Solution Approach 1:
The hearing instrument system performs self-diagnosis and self-adjustment by automatically detecting insertion status through sensors and adjusting operational parameters without requiring external professional intervention, thereby improving fitting accuracy while maintaining simple user operation
Solution Approach 2:
The system continuously monitors sensor data from multiple sensors, processes this information through algorithms, and provides real-time feedback to adjust device settings and guide users through visual or haptic cues, achieving high fitting accuracy through closed-loop control
2Measurement precision
If multiple sensors are used to detect insertion status, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
Multiple sensors of different types (accelerometers, gyroscopes, microphones, temperature sensors) are integrated into a unified sensing system that collectively detects insertion status, combining their individual capabilities to achieve high measurement precision while managing complexity through systematic integration
Solution Approach 2:
The sensor system serves multiple functions simultaneously: detecting insertion status, determining device orientation, monitoring environmental conditions, and guiding user placement, thereby justifying the complexity through multi-functional utility
3Measurement precision
If real-time sensor monitoring and ML model processing are implemented, then the fitting accuracy improves, but the power consumption increases
Solution Approach 1:
The system performs sensor data collection and ML model processing at specific intervals or triggered by events rather than continuously, reducing overall power consumption while maintaining accurate fitting determination through periodic updates
Solution Approach 2:
The ML model and processing algorithms are pre-trained and stored in the device, allowing rapid inference during operation without requiring complex real-time computations, thereby reducing power consumption during actual use
4Ease of operation
If the system provides detailed feedback and guidance, then the ease of operation improves, but the device complexity increases
Solution Approach 1:
A processing system acts as an intermediary between the complex sensor/ML components and the user, translating raw sensor data and model outputs into simple, intuitive visual or haptic feedback that guides users without exposing them to the underlying complexity
Solution Approach 2:
The system replaces complex mechanical adjustment mechanisms with software-based control and digital feedback, allowing sophisticated guidance capabilities to be implemented through algorithms rather than physical mechanisms
Data Source
AI summary
A method for fitting a hearing instrument comprises obtaining sensor data from a plurality of sensors belonging to a plurality of sensor types; applying a machine learned (ML) model to determine, based on the sensor data, an applicable fitting category of the hearing instrument from among a plurality of predefined fitting categories, wherein the plurality of predefined fitting categories includes a fitting category corresponding to a correct way of wearing the hearing instrument and a fitting category corresponding to an incorrect way of wearing the hearing instrument; and generating an indication based on the applicable fitting category of the hearing instrument.