Running Analysis System Data Segmentation
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Solution Overview
Problem
Existing running analysis systems fail to effectively guide users in improving their running performance by overwhelming them with various data points, making it difficult to identify key information for improvement.
Innovation Solution
A running analysis system that includes wearable devices and a server-based analysis platform, which classifies and evaluates running data using classification methods such as average value and standard deviation, and cluster analysis, to provide users with focused insights on their performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If various running data is collected and displayed, then comprehensive running analysis is provided, but user motivation decreases due to information overload
Solution Approach 1:
The patent segments running data into multiple categories (running form, running pace, running distance, etc.) and further divides each category into specific measurement sections. This segmentation allows the system to present comprehensive data in an organized manner, preventing information overload while maintaining complete analysis coverage.
Solution Approach 2:
The patent applies local quality by detecting attention values specific to each measurement section and highlighting only the most relevant data points for each runner. This selective emphasis allows users to focus on critical areas without being overwhelmed by all available data, thereby maintaining motivation while providing comprehensive analysis.
2Measurement precision
If detailed running data is provided, then comprehensive analysis is achieved, but difficulty in identifying key improvement areas increases
Solution Approach 1:
The patent implements feedback mechanisms by comparing current running data against historical data and establishing improvement trends. The system automatically identifies measurement sections showing improvement and highlights them, providing clear feedback to users about their progress and key areas for improvement without requiring manual analysis of detailed data.
Solution Approach 2:
The patent extracts and highlights only the most significant measurement sections that show improvement trends, separating these from the rest of the data. This extraction process makes key improvement areas stand out clearly, reducing the difficulty of identification while maintaining comprehensive measurement precision.
3Ease of manufacture
If classification methods are applied to running data, then data organization is improved, but system complexity increases
Solution Approach 1:
The patent segments the analysis system into modular components: data collection modules for different running parameters, classification modules for organizing data, and presentation modules for displaying results. This segmentation makes the system easier to manufacture and maintain while providing sophisticated data processing capabilities through standardized processing units.
Data Source
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AI summary
In a running analysis system (100), an information acquirer (70) acquires position information and motion information of a user (10) as a runner, measured at each point of passage in a run by a certain measurement device (20). A measurement value acquirer (80) acquires measurement values of multiple types of motion analysis indices indicating a running motion state of a user (10) for each certain unit measurement section, based on the position information and the motion information chronologically consecutive. A classification processing unit (85) classifies measurement values in multiple measurement sections for each type of motion analysis indices, using a certain classification method for classification by property. A mode determination unit (90) determines an output mode of a classified measurement value based on comparison with a certain comparison target.