Training Effect Parameter Calculation Using Heart Rate Monitoring
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Solution Overview
Problem
Existing methods for determining training effect (TE) using Excess Post-Exercise Oxygen Consumption (EPOC) and activity class are inadequate, as they fail to accurately reflect physiological effects during high-intensity workouts, long sessions, discontinuous training, and do not account for individual base endurance, leading to unreliable fitness improvement assessments.
Innovation Solution
A method that utilizes multiple training effect parameters, including peakTE (maximal stress) and baseTE (cumulative physiological load), calculated using heartbeat monitoring data, to provide a comprehensive totalTE (cumulative training effect) that accounts for both maximum stress and base endurance, ensuring accurate fitness improvement assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single training effect parameter (EPOC-based) is used to assess fitness improvement, then the determination method is simple, but the accuracy of reflecting actual physiological effect deteriorates
Solution Approach 1:
The patent divides the training effect determination into multiple independent parameters: EPOC parameter (reflecting homeostatic disturbance), activity class parameter (reflecting exercise intensity), and duration parameter (reflecting exercise time). Each parameter is calculated separately using specific algorithms, allowing comprehensive assessment without excessive complexity in any single calculation method.
Solution Approach 2:
The patent transitions from single-dimensional EPOC-based assessment to multi-dimensional assessment by incorporating activity class classification and duration factors. This adds new dimensions to the training effect evaluation, enabling more accurate reflection of physiological effects across different exercise types and intensities.
2Reliability
If EPOC-based method is used to determine training effect, then the calculation is straightforward, but reliability during discontinuous training sessions deteriorates
Solution Approach 1:
The patent continuously monitors heart rate throughout the entire exercise session, not just during high-intensity periods. This continuous data collection ensures that pauses and intermittent activities are captured, allowing accurate calculation of EPOC and activity class parameters even during discontinuous training sessions with multiple rest periods.
3Measurement precision
If activity class index is used to individualize training effect determination, then the method is simple to implement, but the precision of accounting for individual base endurance deteriorates
Solution Approach 1:
The patent uses activity class classification to determine appropriate parameter ranges and weighting factors for individual assessment. By mapping activity class levels to specific parameter configurations, the system personalizes training effect determination without requiring complex individual physiological profiling, balancing precision with implementability.
Data Source
AI summary
The present disclosure concerns determining physiological training effect of a physiological performance of a person by monitoring the performance using one or more performance-monitoring means in order to obtain performance data, and, according to one aspect of the invention, determining, using computing means capable of utilizing the performance data, a third training effect parameter describing a third physiological effect of the performance using a third determination method, the third physiological effect being a combination effect of the first and second physiological effects which are different from each other and are descriptive of different physiological effects of training, such as homeostatic disturbance and cumulative physiological load, respectively.