Hearing Instrument EMREO Sensors for Eye Movement Detection
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Solution Overview
Problem
Current hearing instruments face challenges in accurately detecting eye movement-related eardrum oscillations (EMREOs) due to environmental factors such as user motion, ambient noise, and user speech, which hinder effective utilization of EMREOs for various user interactions and health monitoring.
Innovation Solution
Incorporation of EMREO sensors within the ear canal of hearing instruments to detect environmental signals of eardrum oscillations, combined with signal processing and machine-learning techniques, enabling the detection and utilization of EMREOs for actions such as changing settings, generating health-related data, and controlling user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EMREO sensors are placed in the ear canal to detect eardrum oscillations, then eye movement detection capability is improved, but detection accuracy deteriorates due to environmental factors such as user motion, ambient noise, and user speech
Solution Approach 1:
The patent uses the eardrum as an intermediary medium to detect eye movements indirectly through EMREO signals. Instead of placing sensors directly on the eyes, the system detects eardrum oscillations caused by eye movements, which are then processed to infer eye movement information. This intermediary approach allows detection while avoiding direct exposure to environmental interference at the eye location.
Solution Approach 2:
The patent replaces direct optical or mechanical eye tracking systems with an acoustic-based detection method. By using microphones to capture EMREO signals from eardrum oscillations and applying signal processing techniques, the system substitutes a mechanical/optical measurement system with an acoustic field-based system that is less susceptible to certain environmental factors.
2Reliability
If signal processing and machine-learning techniques are applied to EMREO signals, then reliability of eye movement detection is improved, but device complexity increases
Solution Approach 1:
The patent applies signal processing and machine-learning techniques in advance to train models that can reliably distinguish EMREO signals from environmental noise. By performing preliminary training and model development, the system establishes robust detection algorithms that can be deployed in the hearing instrument, improving reliability while managing complexity through pre-computed solutions.
Solution Approach 2:
The system uses the hearing instrument's existing processing capabilities to analyze and interpret EMREO signals. Rather than requiring entirely separate dedicated hardware for eye movement detection, the patent leverages the audio processing infrastructure already present in modern hearing instruments, allowing the device to serve multiple functions and reducing overall system complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables reliable detection of EMREOs in real-world conditions, allowing hearing instruments to perform actions based on eye movements, enhancing user interaction and health monitoring capabilities.
Implementation Method 1
eye movement-related eardrum oscillations (EMREOs) of one or more eardrums of a user... EMREO sensors are configured to detect environmental signals of EMREOs of an eardrum
Data Source
AI summary
A set of one or more processing circuits obtains eye movement-related eardrum oscillation (EMREO)-related measurements from one or more EMREO sensors of a hearing instrument. The EMREO sensors are located in an ear canal of a user of the hearing instrument and are configured to detect environmental signals of EMREOs of an eardrum of the user of the hearing instrument. The one or more processing circuits may perform an action based on the EMREO-related measurements.