Personalized Weight Management System Using Characteristic Curves
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Solution Overview
Problem
Existing weight management systems are intrusive, require numerous user inputs, and provide generic recommendations based on statistical data, discouraging users from achieving their target weight.
Innovation Solution
A method and system that use a series of weighings to derive a characteristic curve for an individual, calculating a target number of calories to burn, and providing personalized exercise and diet instructions tailored to their specific curve, eliminating the need for absolute instructions and incorporating current trends and habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If baseline calculations using statistical studies are applied, then weight management recommendations can be provided, but the recommendations become generic and less relevant to individual users
Solution Approach 1:
The system uses the user's own weighing data to automatically generate personalized recommendations. The electronic scale collects weight measurements and body fat percentage over time, and the processing unit automatically derives characteristic curves and calculates personalized calorie targets without requiring external statistical databases or manual input from the user.
Solution Approach 2:
The system transitions from using fixed statistical parameters (age, height, weight, gender) to dynamic individual parameters derived from actual weighing data. By calculating characteristic curves based on real measurements of total weight, fat body mass, and lean body mass over time, the system adapts recommendations to the user's actual physiological trajectory rather than population averages.
2Measurement precision
If intrusive methods measuring body fluid parameters are used, then more accurate weight management can be achieved, but user comfort and ease of use deteriorate
Solution Approach 1:
The system extracts only the necessary measurements (total weight and body fat percentage) from the weighing process, eliminating the need for intrusive body fluid measurements. The electronic scale captures these two parameters automatically during routine weighing, and the processing unit derives all necessary insights from these non-intrusive data points.
Solution Approach 2:
The system replaces intrusive physical measurements (body fluid analysis) with non-intrusive weighing measurements. Instead of requiring blood tests or other invasive procedures, the system uses the electronic scale to obtain weight and body fat percentage data, which are already available from routine self-weighting behavior.
3Adaptability or versatility
If systems requiring large numbers of user inputs are used, then more customized recommendations can be provided, but the complexity and tediousness increase
Solution Approach 1:
The system performs preliminary data collection and processing automatically through the electronic scale before generating recommendations. By continuously collecting weight and body fat percentage data in advance, the system builds characteristic curves that enable personalized recommendations without requiring the user to manually input numerous parameters at the time of recommendation generation.
Solution Approach 2:
The electronic scale and processing unit automatically perform data collection, characteristic curve derivation, and recommendation calculation without requiring active user participation. The system serves itself by using the user's own weighing data to generate personalized advice, eliminating the need for users to provide extensive manual input.
4Ease of operation
If absolute instructions on diet and exercise are provided, then clear guidance can be given, but the need to account for current trends and habits is overlooked
Solution Approach 1:
The system transforms static absolute instructions into dynamic relative instructions by continuously updating recommendations based on the user's current weight trajectory and characteristic curve. The processing unit calculates calorie targets relative to the user's actual weight loss progress and physiological state, making the instructions adaptive to changing conditions rather than fixed.
Solution Approach 2:
The system incorporates feedback loops where the user's actual weighing data continuously informs and adjusts the recommendations. By comparing actual weight changes against target weight loss goals and updating characteristic curves with new measurements, the system provides feedback that adapts instructions to current trends and habits, making recommendations increasingly accurate over time.
Data Source
AI summary
A method for providing personalized recommendations to an individual, relating to monitoring his or her weight, comprising the steps of /a/ obtaining a plurality of prior weighings for the individual concerned, each weighing giving the total weight and the body fat percentage, /b/ deriving a characteristic curve (S1) for the individual by associating at least the total weight and the fat body mass, /c/ obtaining a target total weight (P2) for the user to achieve by the end of a target period, and therefore a target total weight change, /d/ deducing a target number of calories to burn, corresponding to target changes in the fat body mass and lean body mass deduced from the total weight change, /e/ providing the user with personalized instructions on exercise and/or diet.