Sensor-Activated Food Intake Tracking Without Manual Logging
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Solution Overview
Problem
Existing methods for tracking food intake are cumbersome, require significant human intervention, lack real-time feedback, and fail to provide insights into eating behavior, and are not socially acceptable or adaptable to various meal scenarios.
Innovation Solution
A system using wearable sensors and machine learning to autonomously detect food intake events, track parameters such as eating pace and content, and provide real-time feedback without requiring user intervention, adaptable to diverse dining settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual food intake tracking methods (written diaries, software applications) are used, then food intake can be recorded, but the accuracy of human-entered information is limited and the process is cumbersome and time-consuming
Solution Approach 1:
The system enables automatic self-tracking of food intake through sensors that detect eating events, eliminate manual data entry, and automatically log consumption information. The wearable device autonomously monitors eating behavior without requiring user intervention, thus improving accuracy while reducing operational burden.
Solution Approach 2:
The patent replaces manual mechanical processes (writing in diaries, typing in software) with automated sensor-based detection systems. Sensors detect eating events through physiological signals or motion patterns, substituting human manual operations with automated electronic detection and recording mechanisms.
2Loss of information
If manual food journaling software is used, then food intake content can be tracked, but real-time feedback about eating habits is not provided
Solution Approach 1:
The system incorporates real-time feedback mechanisms where sensors continuously monitor eating events and immediately process the data. The system provides instantaneous feedback about eating habits, portion sizes, and nutritional content through the wearable device or connected mobile application, enabling users to adjust their behavior in real-time rather than reviewing data later.
3Extent of automation
If tableware with built-in sensors is used, then automatic food intake tracking is achieved, but the device is not adaptable to various meal scenarios and dining settings
Solution Approach 1:
The wearable device is designed with multi-functional sensor capabilities that can detect various types of eating events (chewing, swallowing, utensil handling) across different dining contexts. The system adapts to different meal scenarios by recognizing patterns in sensor data regardless of whether the user is eating at home, in a restaurant, or on the go, making it universally applicable to diverse dining settings.
Solution Approach 2:
The system dynamically adjusts its monitoring and detection parameters based on the detected dining context. By analyzing patterns in sensor data such as motion intensity, frequency, and type, the system automatically adapts its tracking behavior to suit different meal scenarios, transitioning between passive and active monitoring modes as needed.
4Loss of information
If sensors continuously monitor eating behavior, then comprehensive data is collected, but energy consumption increases
Solution Approach 1:
The sensor system operates in periodic cycles rather than continuously, activating monitoring during detected eating events and entering low-power mode between events. The system uses motion sensors or other triggers to detect when eating is occurring, then intensifies monitoring only during those periods, reducing overall energy consumption while maintaining data completeness.
Solution Approach 2:
The system applies partial monitoring by focusing sensor attention only on relevant eating-related activities rather than continuously monitoring all body functions. By selectively activating specific sensors and processing algorithms only when eating events are detected, the system achieves comprehensive eating data with minimal energy expenditure.
Data Source
AI summary
Techniques disclosed herein relate generally to monitoring and tracking food intake events or other behaviors. In some examples, the techniques involve detecting, based on at least one of user input or sensor input from a first set of one or more sensors, a start of a food intake event; activating, in response to detecting the start of the food intake event, a second set of one or more sensors for tracking the food intake event; and determining, based on at least sensor data from the second set of one or more sensors, one or more event-specific parameters for the food intake event.