Wearable Food Intake Tracking With Real-Time Behavioral Feedback
Find Innovative SolutionsGenerate Solutions
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 behaviors, and are not socially acceptable or discreet in various dining settings.
Innovation Solution
A sensing device with accelerometers, gyroscopes, and cameras that autonomously detects food intake events, tracks eating behaviors, and provides real-time feedback without requiring user intervention, capable of handling diverse meal scenarios and social settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual food logging is used, then users can track food intake, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system automatically detects food intake events using sensors (accelerometers, gyroscopes, microphones) and processes images without requiring user intervention. The wearable device autonomously identifies eating gestures, captures relevant data, and logs food intake information, eliminating the need for manual entry while maintaining tracking accuracy.
Solution Approach 2:
The patent replaces manual mechanical logging with automated sensor-based detection. Accelerometers and gyroscopes detect eating gestures through motion patterns, microphones capture chewing sounds, and image processing algorithms analyze food visuals, substituting the manual writing/typing process with automated electronic sensing and processing systems.
2Ease of operation
If automated sensor-based tracking is implemented, then manual burden is reduced, but real-time feedback capability is lacking
Solution Approach 1:
The system incorporates real-time feedback mechanisms that provide users with immediate information about their eating behaviors. The processor analyzes sensor data during food intake events and delivers feedback through the wearable device interface, enabling users to adjust their eating habits based on real-time insights rather than waiting for later analysis.
3Productivity
If traditional food tracking methods are used, then basic logging is achieved, but insights into eating behaviors are not provided
Solution Approach 1:
The patent adds behavioral dimensionality to food tracking by analyzing not just what is eaten but how it is eaten. Sensors capture gestures, movements, chewing patterns, and temporal characteristics, transforming basic food logging into comprehensive eating behavior analysis that provides insights into pace of eating, portion control, and dietary habits.
Solution Approach 2:
The system performs preliminary analysis of eating behaviors during the food intake event itself, capturing and processing gesture data, image data, and sensor information in real-time or near-real-time. This preliminary action enables immediate behavioral insights rather than requiring post-processing of recorded data.
4Measurement precision
If portable spectrometers are used for food identification, then food composition analysis is possible, but human intervention is required
Solution Approach 1:
The wearable device integrates multiple sensing modalities (accelerometers, gyroscopes, microphones, image sensors) into a single universal system that can detect various aspects of food intake through different physical phenomena. This multi-functional approach replaces the need for specialized devices like portable spectrometers while eliminating manual intervention through automated sensor fusion and processing.
5Measurement precision
If automated detection systems are deployed, then tracking accuracy improves, but social acceptance decreases due to lack of discretion
Solution Approach 1:
The system uses a wearable device with a flexible, discreet form factor that can be comfortably worn close to the body. The thin-film sensors and compact design allow the device to remain unobtrusive during social interactions, maintaining tracking accuracy while minimizing social impact through its unnoticeable presence.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A sensing device monitors and tracks food intake events and details. A processor, appropriately programmed, controls aspects of the sensing device to capture data, store data, analyze data and provide suitable feedback related to food intake. More generally, the methods might include detecting, identifying, analyzing, quantifying, tracking, processing and/or influencing, related to the intake of food, eating habits, eating patterns, and/or triggers for food intake events, eating habits, or eating patterns. Feedback might be targeted for influencing the intake of food, eating habits, or eating patterns, and/or triggers for those. The sensing device can also be used to track and provide feedback beyond food-related behaviors and more generally track behavior events, detect behavior event triggers and behavior event patterns and provide suitable feedback.