Weight Trend Prediction via Water Weight Oscillation Filtering
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Solution Overview
Problem
Obesity management is challenging due to fluctuating weight caused by water retention, leading to demotivation and difficulty in tracking consistent weight loss trends, as existing systems require extensive data collection and are inconvenient, making it hard for users to maintain weight loss efforts.
Innovation Solution
A system that uses weight readings over three weeks to predict future weight trends by filtering out water weight fluctuations, providing users with predictions on weight gain, maintenance, or loss through a user-friendly interface, using a scale and mobile devices for data collection and analysis, and delivering insights on weight oscillations and momentum.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weight measurements are taken daily to track weight loss progress, then more data is collected for analysis, but the measurements are contaminated by water weight fluctuations that mask real weight trends
Solution Approach 1:
The system extracts and removes the water weight component from total body weight measurements. By analyzing the oscillating pattern of daily weight changes and identifying the periodic water retention component, the system separates this noise from the underlying true weight trend, allowing accurate weight loss tracking despite daily fluctuations
Solution Approach 2:
The system performs preliminary analysis of weight measurement patterns to establish the user's typical water weight oscillation profile before using this information to interpret future measurements. By learning the characteristic water weight pattern in advance, the system can automatically compensate for these fluctuations in subsequent weight tracking
2Loss of information
If existing weight monitoring systems collect extensive data on diet, activity, body fat, and other parameters, then more comprehensive information is available for analysis, but the system becomes complex and inconvenient for users
Solution Approach 1:
The system extracts and utilizes only the essential weight measurement data, discarding the need for extensive additional data collection on diet, activity, and other parameters. By focusing solely on weight measurements and applying signal processing to extract the underlying trend, the system achieves effective weight monitoring with minimal user burden
Solution Approach 2:
The system performs automatic analysis of weight measurement patterns without requiring user input or interpretation. The algorithm autonomously identifies water weight fluctuations, separates them from true weight changes, and provides weight trend information, eliminating the need for users to manually track or report additional data
3Duration of action of moving object
If weight loss efforts are pursued over extended periods, then sustained weight loss should occur, but plateaus develop that demotivate users and cause them to abandon their efforts
Solution Approach 1:
The system provides continuous feedback to users about their true weight loss progress by filtering out water weight noise. This accurate feedback prevents misinterpretation of temporary fluctuations as plateaus, maintaining user motivation and adherence to weight loss programs over the long term
Solution Approach 2:
The system acts as an intermediary between raw weight measurements and user interpretation. By introducing the signal processing algorithm as a mediator that separates water weight oscillations from true weight trends, the system provides reliable weight loss information that maintains user confidence and motivation during extended weight loss journeys
Data Source
AI summary
A system and method for determining the weight gain trend of a user. The weekly weight oscillation is determined, and a future weight trend can be predicted. At least three weeks of weight oscillation trends are typically used to predict future weight trends.