Fitness Test Application Using Segmented Exercise Data Analysis
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Solution Overview
Problem
Current methods for assessing cardiorespiratory fitness lack accuracy and fail to dynamically adjust training plans based on changing fitness levels, providing only rough estimates and not guiding users to physiological target states during free exercise sessions.
Innovation Solution
A system that segments and evaluates exercise data to accurately estimate cardiorespiratory fitness by selecting reliable data segments, calculating weighting coefficients, and updating training plans based on changing fitness levels, using heart rate and performance data from various exercise types, including outdoor and indoor activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If free exercise sessions are used for fitness assessment, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the continuous exercise data into distinct phases (warm-up, steady-state, cooldown) and identifies reliable data segments within each phase. This segmentation allows the system to select only the most reliable portions of free exercise data for fitness assessment, thereby maintaining measurement precision while preserving the ease of free exercise operation.
Solution Approach 2:
The patent applies different quality criteria to different segments of exercise data. Instead of treating all data equally, it evaluates each segment's reliability based on local conditions (heart rate stability, speed consistency, environmental factors) and weights them accordingly. This local quality assessment ensures high measurement precision from specific reliable segments while maintaining overall system ease of operation.
2Device complexity
If simple mathematical calculations are used for fitness estimation, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors exercise data quality and adjusts the selection of data segments accordingly. It uses feedback loops to identify reliable segments based on physiological responses (heart rate, power output) and environmental conditions, then weights these segments to produce accurate fitness estimates. This feedback-driven approach achieves high measurement precision without requiring complex hardware, maintaining low device complexity.
Solution Approach 2:
The patent changes the parameters used for fitness calculation dynamically based on data reliability. Instead of using fixed simple formulas, it adjusts weighting coefficients for different data segments based on their reliability metrics (heart rate stability, speed consistency, environmental factors). This parameter adjustment allows accurate fitness estimation using relatively simple computational methods, balancing device complexity and measurement precision.
3Measurement precision
If data segmentation and evaluation are implemented, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides exercise data into meaningful segments (warm-up, steady-state, cooldown phases) and evaluates each segment's reliability independently. This segmentation approach enables precise fitness measurement by selecting only reliable segments while avoiding the need for complex overall data processing, thus limiting the increase in device complexity.
Solution Approach 2:
The patent uses dynamic parameter adjustment through weighting coefficients to balance measurement precision and device complexity. By changing the weights of different data segments based on their reliability metrics, the system achieves high measurement precision using computationally efficient methods that don't require excessively complex device architecture.
4Measurement precision
If multiple exercise sessions are combined for fitness estimation, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent segments data from multiple exercise sessions and evaluates each segment's reliability. By identifying and combining only the reliable segments across sessions, the system achieves improved measurement precision without requiring the user to complete unnecessarily long exercise protocols, thus minimizing time loss while maintaining accuracy.
Solution Approach 2:
The patent applies local quality assessment to data from multiple exercise sessions, weighting each segment based on its reliability characteristics. This approach allows the system to efficiently combine data from multiple sessions to improve precision without requiring excessive exercise time, as only the most reliable segments are heavily weighted in the final calculation.
Data Source
AI summary
This document describes a fitness test application which allows the user to exercise freely (running and cycling outdoors, bicycle ergometer, rowing ergometer, treadmill) and which provides an estimate of user's fitness during and/or after the exercise. The analysis can be performed either as real-time or as post-analysis.