Weight Control Profile Computation via Wearable Sensor Data
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Solution Overview
Problem
Existing activity monitoring systems lack effective algorithms for providing personalized guidance on physical activity and nutrition intake adjustments to help users achieve weight development targets, relying on manual input and limited data analysis.
Innovation Solution
An integrated activity monitoring system that combines wearable sensors, such as accelerometers and heart rate monitors, with a server-based algorithm to compute and display activity and nutrition adjustment instructions, allowing users to set targets and receive real-time guidance on physical activity and nutrition intake adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual input and limited data analysis are used in activity monitoring systems, then device complexity is reduced, but the quality of personalized guidance and weight management effectiveness deteriorates
Solution Approach 1:
The system automatically collects activity data through wearable sensors and performs computations to generate personalized weight management guidance without requiring manual user input for data entry. The algorithm self-adjusts recommendations based on continuous data analysis, eliminating the need for complex manual configuration while maintaining high guidance quality
Solution Approach 2:
The system dynamically adjusts activity targets and nutrition recommendations by changing parameters based on continuous analysis of user data including current weight, weight发展目标, and measured physical activity. This allows personalized guidance to adapt automatically without increasing system complexity
2Measurement precision
If continuous activity and nutrition data are analyzed, then guidance accuracy is improved, but data processing requirements and system complexity increase
Solution Approach 1:
The data processing is divided into segments: wearable sensors collect specific physical activity data, the server computer receives and stores this data separately, and the algorithm processes the segmented data to compute specific adjustments to activity targets and nutrition intake. This segmentation reduces overall system complexity while maintaining high guidance accuracy
Solution Approach 2:
A server computer acts as an intermediary between wearable sensors and the algorithm, receiving, storing, and managing activity data before processing. This intermediary layer simplifies the architecture by separating data collection from data processing, reducing complexity while enabling continuous data analysis for accurate guidance
3Reliability
If personalized weight management guidance is provided, then weight management effectiveness is improved, but the need for multiple sensors and data collection points increases device complexity
Solution Approach 1:
The wearable activity monitoring device performs multiple functions: it measures physical activity through sensors, transmits data to the server, and provides feedback to the user. This multi-functionality reduces the need for separate dedicated devices while maintaining reliable weight management effectiveness through integrated data collection and processing
Data Source
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AI summary
There is provided a method comprising: acquiring, in an apparatus, at least one weight value representing weight of a user; acquiring, in an apparatus, a weight development target for the user; computing, on the basis of the at least one weight value and the weight development target, at least one activity adjustment instruction indicating required the physical activity of the user; and outputting the activity adjustment instruction through an interface.