ECG-Based Menu Recommendation for Real-Time Nutrient Balancing
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Solution Overview
Problem
Conventional menu recommendation services do not account for real-time changes in a user's health condition, requiring burdensome actions like periodic blood draws to determine nutrient needs, and fail to consider dynamic factors such as recent activity or food intake.
Innovation Solution
A method using electrocardiogram signals to identify lacked and surplus nutrients through pre-trained artificial neural networks, recommending a personalized menu to supplement deficiencies and avoid excess nutrients without direct blood testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional menu recommendation services use static factors (height, weight, obesity level) to determine nutrient needs, then the recommendation process is simple and easy to operate, but the accuracy of nutrient assessment is insufficient and cannot reflect real-time health conditions
Solution Approach 1:
The patent introduces an electrocardiogram (ECG) signal as an intermediary to indirectly assess nutrient status. Instead of directly measuring nutrients through invasive blood draws, the system uses ECG signals as a mediator that reflects the body's nutritional state, particularly electrolyte levels, thereby improving assessment accuracy without increasing operational complexity for users
Solution Approach 2:
The patent replaces the mechanical/invasive method of blood drawing with a non-invasive electrical signal measurement (ECG). By substituting the physical extraction of blood with electrical signal detection, the system achieves more accurate real-time nutrient assessment while maintaining ease of operation and eliminating painful procedures
2Measurement precision
If periodic blood draws are used to determine health conditions and nutrient needs, then accurate nutrient assessment can be achieved, but the procedure becomes invasive and burdensome for users
Solution Approach 1:
The patent substitutes the mechanical process of blood extraction with an electrical signal measurement system. ECG devices detect electrical signals from the heart through skin contact, replacing the need for needles and blood collection tubes, thereby eliminating invasiveness while maintaining health assessment accuracy
Solution Approach 2:
The ECG signal serves as an intermediary that provides indirect but accurate information about nutrient status, particularly electrolyte levels. This intermediary approach allows the system to assess health conditions without direct contact with bodily fluids, eliminating the harmful aspects of blood draws while preserving measurement precision
3Adaptability or versatility
If conventional services only consider general health conditions based on static factors, then the recommendation system is simple to implement, but it cannot adapt to real-time changes in health status, activity level, or recent food intake
Solution Approach 1:
The patent transforms the static menu recommendation system into a dynamic one by continuously monitoring ECG signals. The system adapts to real-time changes in health status, activity level, and nutritional needs by processing sequential ECG data, allowing menu recommendations to evolve with the user's current state rather than remaining fixed based on initial measurements
Solution Approach 2:
The patent implements a feedback mechanism where ECG signals continuously provide information about the user's current health and nutritional state. This feedback loop allows the system to adjust menu recommendations in real-time based on detected changes in electrolyte levels, heart rate variability, and other physiological parameters reflected in the ECG signal
Data Source
AI summary
The present invention proposes a method of recommending a menu suitable for a user's real-time health condition based on an electrocardiogram (ECG). The menu recommendation method may include: obtaining an electrocardiogram signal measured for a user; identifying one or more lacked nutrients and one or more surplus nutrients corresponding to the user's health condition by analyzing the electrocardiogram signal using pre-trained artificial intelligence (AI); and setting a menu including at least one food that can supplement the identified lacked nutrients and avoid the identified surplus nutrients. Through this, a user can receive a recommendation for a menu optimized for the user's health condition at that time simply by measuring an electrocardiogram.