Contextual Drink Detection Using Wearable Sensor Fusion
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Solution Overview
Problem
Existing methods for hydration detection in wearable devices inaccurately identify drinking motions, leading to underestimated hydration levels due to misclassification of other motions.
Innovation Solution
A system utilizing wearable devices with motion and biological sensors to detect drink events by correlating motion signals with biological indicators, employing machine learning algorithms and adaptive signal processing to distinguish between drinking and non-drinking motions, and providing real-time hydration monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If motion sensors alone are used to detect drinking motions, then the detection process is simple, but the accuracy of drink detection deteriorates due to misclassification of other motions
Solution Approach 1:
The patent combines motion sensor data with biological sensor data (heart rate, respiration rate, skin temperature) to detect drinking events. This multi-sensor fusion approach merges different types of physiological signals to improve detection accuracy while compensating for the limitations of individual sensors.
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary processing layer that analyzes and correlates data from multiple sensors. This intermediary system identifies patterns and relationships between motion, heart rate, respiration, and temperature changes to accurately distinguish drinking events from other activities.
2Measurement precision
If multiple sensors and machine learning algorithms are used to improve drink detection accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The wearable device integrates multiple sensors (motion, heart rate, respiration, skin temperature) into a single multi-functional platform. This universal device serves multiple purposes including drink detection, hydration monitoring, and physical activity tracking, thereby distributing the complexity across various functions rather than requiring separate dedicated systems.
Solution Approach 2:
The system employs machine learning algorithms that automatically adapt and learn from user-specific patterns over time. This self-service capability allows the system to improve its detection accuracy for each individual user without requiring manual calibration or complex configuration, thereby managing complexity through automated adaptation.
3Ease of operation
If motion data alone is used for drink detection, then ease of operation is maintained, but reliability of hydration monitoring deteriorates due to misidentification of motions
Solution Approach 1:
The system continuously monitors multiple physiological parameters and uses feedback loops to adjust detection thresholds and algorithms. By comparing real-time data from motion, heart rate, respiration, and temperature sensors against learned patterns, the system reliably distinguishes drinking events from other activities while maintaining automated operation that requires minimal user intervention.
Data Source
AI summary
A system and method operable to monitor hydration and drink activity using one or more body-worn sensors and contextual information to more accurately detect drinking motions made by the user. The system and method can use an application encoded on a non-transitory computer-readable medium to receive disparate data from the one or more sensors to determine if the user has made a drinking motion. The analysis can be further refined using contextual information and a variable threshold to more accurately identify drinking motions.