Training Load Calculation via Protein Consumption and Mechanical Stimulus
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Solution Overview
Problem
Conventional methods for determining training load and recovery time during exercise sessions are inaccurate and insensitive to exercise history, particularly when dealing with multiple training sessions involving different modalities like continuous training, strength training, or sports activities.
Innovation Solution
A method and apparatus that calculate training load by considering protein consumption based on carbohydrate reserves, using heart rate and activity information to determine instantaneous protein expenditure, and estimate recovery time by accounting for mechanical stimulus and energy expenditure during and after exercise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods like EPOC (Excess Post-exercise Oxygen Consumption) are used to determine training load and recovery time, then the determination process is simplified, but the accuracy and sensitivity to exercise history deteriorate
Solution Approach 1:
The training load determination is segmented into multiple independent components: protein consumption calculation, carbohydrate reserve tracking, mechanical stimulus assessment, and recovery time estimation. Each component can be calculated separately and then integrated, improving accuracy while maintaining manageable complexity through modular computation.
Solution Approach 2:
The system performs preliminary calculations of carbohydrate reserves and protein consumption rates before determining final training load. By pre-calculating these metabolic parameters based on exercise history and intensity, the system builds an accurate foundation for the final determination without requiring complex real-time analysis.
2Adaptability or versatility
If conventional EPOC methods are used, then the calculation process is straightforward, but sensitivity to exercise history and multiple training modalities deteriorates
Solution Approach 1:
The calculation model dynamically adapts to different exercise modalities and exercise history by adjusting the weighting of various parameters. The system continuously updates carbohydrate reserve levels and protein consumption rates based on previous exercise sessions, enabling sensitive detection of cumulative training effects across multiple modalities while maintaining a unified calculation framework.
3Measurement precision
If protein consumption based on carbohydrate reserves is considered, then training load accuracy is improved, but computational complexity increases
Solution Approach 1:
The system uses readily available data from standard exercise monitoring (heart rate, exercise intensity, duration) to self-determine carbohydrate consumption and reserve levels. By deriving protein consumption from these existing measurements through established metabolic relationships, the system achieves high training load accuracy without requiring additional complex measurements or calculations.
Data Source
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AI summary
A method of determining a user's training load, includes determining protein combustion information and determining the user's training load based on the protein combustion information. The protein combustion information may be determined using heart rate information, activity information, carbohydrate reserve information determined using a carbohydrate combustion model and carbohydrate reserve information representing the user's carbohydrate reserves at a beginning of at least one exercise session, fitness information, intensity information, nutritional intake information representing nutritional intake during at least one exercise session, and/or a fat combustion model. The user's training load may be determined based on a mechanical stimulus and/or a modality. Recovery time information may be determined based on the training load. A corresponding apparatus and computer-readable medium correspond to the above method.