Food Image Recognition for Personalized Nutrient Intake Tracking
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Solution Overview
Problem
Existing methods for dietary intake assessment, such as weighing, meal reviews, and food frequency questionnaires, are laborious, time-consuming, or inaccurate, failing to efficiently and accurately reflect long-term dietary intake and nutrient ratios.
Innovation Solution
A deep learning-based food image recognition system that segments and identifies food types, computes nutrient amounts, and adjusts intake based on personalized thresholds, using a trained model for efficient and accurate nutrition management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weighing method is used to obtain accurate dietary intake information, then measurement precision is improved, but productivity deteriorates due to time-consuming and laborious operations
Solution Approach 1:
The patent replaces the mechanical weighing system with an automated image recognition system using deep learning. The food image recognition model processes photographs of meals to automatically identify food types and estimate portions, eliminating the need for manual weighing while maintaining measurement accuracy.
Solution Approach 2:
The patent creates a visual copy of the actual food intake through photographs. Instead of physically measuring each food item, the system captures images that serve as replicas of the consumed food, which are then analyzed by the recognition model to extract nutritional information.
2Productivity
If meal review method is used to reduce time consumption, then productivity is improved, but measurement precision deteriorates due to reliance on subject memory and recall accuracy
Solution Approach 1:
The patent replaces the human memory-based recall system with an automated image analysis system. Instead of relying on subjects to remember and report what they ate, the system uses deep learning models to objectively analyze photographs of the actual food consumed, eliminating memory errors and subjective bias.
Solution Approach 2:
The patent introduces food photographs as an intermediary between the actual food intake and the assessment process. The images serve as objective records that bridge the gap between what was consumed and what is measured, providing verifiable evidence rather than relying on subjective recall.
3Productivity
If food image recognition model is used to improve productivity, then diet assessment efficiency is improved, but device complexity increases due to deep learning model training and processing requirements
Solution Approach 1:
The patent performs preliminary processing by training the deep learning model in advance using a comprehensive food image dataset. The model is pre-trained to recognize various food types, portions, and characteristics, so that during actual use, the system can quickly process images without requiring complex real-time analysis or extensive computational resources.
Data Source
AI summary
The present invention includes techniques for food image processing and particularly relates to a method and system for personalized nutrition management with food image recognition models using deep learning. The method comprises: a user side obtains an food image to be taken by a user, and the food image is input into a trained food image recognition models using deep learning to obtain different types of food sub-images; computing the amount of nutrients contained in the food sub-images, and accumulating the nutrients in all the food to obtain the total nutrients intake of the user; setting intake thresholds of various nutrients, and comparing the total intake of various nutrients with corresponding nutrient intake thresholds to obtain a comparison result; according to the comparison result, type and quantity of taken food are adjusted, and nutrition management is completed. The invention associates the food intake information uploaded by the user with other data sets (e.g., recommendations from their nutritional physician) through the server to determine whether the obtained energy and nutrient ratio are appropriate, and finally, the analyzed data is feedbacked to the user, thereby prompting the user to improve the diet plan.