Method for analyzing eating behavior based on biological signals

A service server that communicates with at least one wearable device worn on or attached to a user's body and/or a user device connected to the wearable device to analyze biological signals and predict eating behavior, the service server comprising:

• a communication unit configured to receive body temperature information, heart rate information, and blood pressure information from the wearable device;
• a data processing unit configured to generate body temperature derivative information representing a rate of body temperature change per unit time (dT/dt) from the body temperature information, and generate body temperature integral information representing a time-integrated cumulative value of body temperature elevation relative to a baseline body temperature, and generate a cardiovascular response index from the heart rate information and the blood pressure information;
• an AI analysis unit configured to receive the body temperature derivative information, the body temperature integral information, and the cardiovascular response index as inputs and predict eating behavior, calorie intake, and nutrient composition ratio;
• a database configured to store data from a plurality of users and maintain user-specific customized models;
• and
• a result providing unit configured to transmit prediction results to the user device or link the prediction results with a medical institution server.

A method in which a wearable device worn on or attached to a user's body collects biometric information and an edge AI unit included in the wearable device analyzes the collected biometric information to predict eating behavior, the method comprising:

• collecting, by the wearable device, body temperature information, heart rate information, and blood pressure information through a body temperature measurement unit, a heart rate measurement unit, and a blood pressure measurement unit, respectively;
• generating, by the edge AI unit of the wearable device, body temperature derivative information representing a rate of body temperature change per unit time (dT/dt) from the body temperature information, and body temperature integral information representing a time-integrated cumulative value of body temperature elevation relative to a baseline body temperature;
• calculating, by the edge AI unit, a change rate ratio of the heart rate information and the blood pressure information to generate a cardiovascular response index;
• inputting, by the edge AI unit, the body temperature derivative information, the body temperature integral information, and the cardiovascular response index into a machine learning model to predict eating behavior, meal amount, and/or nutrient composition;
• and
• providing, by the wearable device, the prediction result to a user through an output unit or transmitting the prediction result to a user device or a service server.