Personalized Weight Management via IoT Prediction Model
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current weight management techniques are cumbersome and lack personalized approaches, failing to effectively learn and predict weight changes based on individual food intake and exercise information, leading to low motivation and high costs.
Innovation Solution
A personalized weight management apparatus and method that collects and analyzes daily weight, food, and exercise data to generate predictions for the next day, updating a prediction model to provide tailored food and exercise recommendations for achieving a target weight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If self-management with continuous weight measurement and recording is implemented, then weight management can be performed, but it becomes cumbersome and difficult to implement due to busy life patterns
Solution Approach 1:
The system automatically collects weight data, food intake information, and exercise data without requiring manual recording by the user. The prediction model automatically generates weight change predictions and provides recommendations, eliminating the need for users to continuously measure and record data themselves.
Solution Approach 2:
The system provides automated feedback through prediction results and recommendations based on collected data. The prediction model continuously learns from actual weight changes and adjusts predictions, providing users with actionable insights without requiring manual intervention.
2Reliability
If diet foods with different medicinal plants are used, then weight management can be achieved, but it becomes difficult for users to distinguish and select suitable foods for their physical constitution
Solution Approach 1:
The system tailors food and exercise recommendations to each user's specific physical constitution, weight loss goals, and personal characteristics. The prediction model adjusts recommendations based on individual data patterns, providing personalized guidance rather than generic advice.
Solution Approach 2:
The system varies food and exercise recommendations based on changing parameters such as current weight, weight loss progress, and individual response patterns. The prediction model dynamically adjusts suggestions as users progress through their weight management journey.
3Reliability
If unsafe food is ingested, then weight management may occur, but serious side-effects may occur
Solution Approach 1:
The prediction model acts as an intermediary between the user and food selection, analyzing food safety and suitability before recommending intake. The system filters and validates food options based on user profile and safety criteria, preventing unsafe food recommendations.
4Reliability
If drug administration and procedures are used for weight management, then weight loss can be achieved, but it results in cost burden
Solution Approach 1:
The system replaces expensive drug-based weight management solutions with a cost-effective digital prediction model and personalized guidance system. The low-cost automated system provides continuous monitoring and adjustment without the high costs associated with medical procedures and medications.
Data Source
AI summary
The present disclosure relates to an apparatus for managing an individual's weight, which operates in an Internet of Things environment over a 5G communication network and is capable of effectively managing an individual's weight, and a personalized weight management method using the apparatus. The present disclosure is directed to learning a predicted weight of an individual calculated based on weight information of the individual, information on a type and an amount of food ingested by the individual, and information on exercise performed by the individual, received on the present day, and predicting information on food to be ingested and information on exercise to be performed in order to achieve a target weight received from the individual, based on the learned predicted weight.