Hearing Aid Intake Detection via Multi-Sensor Fusion
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Solution Overview
Problem
Hearing devices lack effective methods for reliably detecting and quantifying fluid, food, and medication intake, particularly in elderly users, who may experience dehydration, poor eating habits, and medication non-adherence, leading to health issues such as dizziness, mental decline, obesity, and heart diseases.
Innovation Solution
A hearing system equipped with microphones and various sensors, including accelerometers and physiological sensors, uses machine learning algorithms to differentiate between fluid, food, and medication intake, providing accurate detection and quantification, and generates outputs to counsel users on hydration and medication adherence through augmented reality and interactive interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If hearing devices use simple sensors to detect fluid intake, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent combines multiple sensor types (acoustic sensors, vibration sensors, temperature sensors, flow sensors) within the hearing device to detect fluid intake. By merging these different sensing modalities, the system achieves reliable detection without requiring a single complex sensor, thus maintaining device simplicity while improving measurement precision.
Solution Approach 2:
The hearing device is designed to perform multiple functions: traditional hearing assistance plus fluid intake detection. The sensor system is configured to detect various parameters (sound, vibration, temperature, flow) that can indicate fluid intake events, making the device multi-functional without significantly increasing complexity.
2Measurement precision
If hearing devices use multiple sensors and machine learning algorithms to differentiate intake types, then measurement precision improves, but device complexity increases
Solution Approach 1:
The detection process is segmented into distinct phases: sensor data collection, feature extraction, pattern recognition using machine learning algorithms, and classification of intake type. This segmentation allows complex processing to be broken down into manageable steps, improving precision while keeping the overall system architecture clear and maintainable.
Solution Approach 2:
The patent introduces intermediate processing layers between the sensors and the final classification output. Machine learning algorithms act as intermediaries that process raw sensor data, extract relevant features, and generate classification results. This intermediary processing enables accurate differentiation of intake types without requiring direct complex interactions between multiple sensors.
3Reliability
If hearing devices continuously monitor intake behavior, then reliability of health monitoring improves, but energy consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system uses periodic sampling of sensor data to detect fluid intake events. The machine learning algorithms process sensor inputs at intervals, identifying patterns that indicate intake behavior. This periodic approach maintains reliable health monitoring by capturing sufficient data points while significantly reducing energy consumption compared to continuous processing.
Solution Approach 2:
The hearing device autonomously processes sensor data using embedded machine learning algorithms, making decisions about when intake events occur without requiring constant external intervention. The system self-manages the monitoring process, activating processing only when sensor patterns suggest potential intake events, thereby improving reliability while conserving energy.
Data Source
AI summary
A method for detecting and quantifying a liquid and/or food and/or medication intake of a user wearing a hearing device which comprises at least one microphone. The method comprises: receiving an audio signal from the at least one microphone and/or a sensor signal from at least one further sensor; and collecting and analyzing the received audio signal and/or further sensor signals so as to detect each time the user drinks and/or takes medication and/or eats something, wherein drinking and/or medication intake is distinguished from eating and/or wherein drinking is distinguished from medication intake, and so as to determine values indicative of how often this is detected and/or a respective amount of liquid and/or food and/or medication ingested by the user.