Physiological Response Prediction Using Food Image Recognition
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Solution Overview
Problem
Individuals with diabetes face challenges in maintaining stable blood sugar levels due to difficulties in accurately estimating carbohydrate intake, tracking glycemic index values, and managing portion sizes, especially in social settings or with unfamiliar foods.
Innovation Solution
A system utilizing extended reality technology that accesses images of consumables and user state information to predict physiological responses, such as blood glucose levels, and provides outputs like recommended actions or therapeutic delivery configuration updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individuals manually track and estimate carbohydrate intake and GI values, then they can manage their blood sugar levels, but it becomes difficult to consistently track and remember GI values especially in social settings or with unfamiliar ingredients
Solution Approach 1:
The system enables automatic food identification and carbohydrate content determination through image recognition technology. Users simply capture images of their food, and the system automatically analyzes the image to identify the food type, estimate portion size, and calculate carbohydrate content and GI value, eliminating the need for manual tracking and remembering of nutritional information
Solution Approach 2:
The patent replaces manual mechanical tracking methods with automated optical recognition systems. Image sensors capture food images, which are then processed by machine learning models to automatically determine nutritional parameters, substituting the manual cognitive process of tracking and remembering with an automated visual recognition system
2Measurement precision
If individuals carefully monitor portion sizes to manage blood sugar levels, then they can achieve better glucose control, but accurately estimating portion sizes becomes challenging especially with larger portions served at restaurants or social pressures to indulge
Solution Approach 1:
The system introduces an intermediary computational model that acts as a mediator between the visual appearance of food and its actual nutritional content. The machine learning model trained on reference images serves as an intermediary that objectively determines portion size and carbohydrate content, filtering out the distorting influence of external factors like restaurant portion sizes or social pressure
Solution Approach 2:
The system creates a digital copy or representation of the actual food portion through image capture. This visual copy is then analyzed by the machine learning model to determine the true portion size and nutritional content, providing an objective measurement that is independent of external influencing factors or subjective estimation
3Measurement precision
If the system incorporates multiple user state factors such as stress state, disease state, blood alcohol level, medication usage, and physical activity level, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The machine learning model is designed as a universal predictive system that can process multiple different types of input parameters (carbohydrate content, user state information including stress, disease state, blood alcohol level, medication usage, and physical activity level) through a single integrated framework. This multi-functional approach allows the system to incorporate numerous factors without proportionally increasing complexity, as all inputs are processed by the same computational model
Data Source
AI summary
A system for predicting and/or managing physiological responses is configurable to (i) access one or more images depicting one or more consumables able to influence a physiological condition of a user, the one or more images being associated with one or more timepoints; (ii) access user state information associated with the user; (iii) determine a predicted physiological response to consumption of the one or more consumables by the user based on at least the one or more images and the user state information; and (iv) present an output based on the predicted physiological response to the user via a user interface.