HRV-Based Training Instruction Generation for Load Range Selection
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Solution Overview
Problem
Existing methods for generating physical training instructions fail to adequately account for the current physical and recovery condition of the individual, leading to unreliable recommendations for optimal load intensities, risking overtraining or undertraining.
Innovation Solution
Selecting a load range based on the current physical condition, incorporating previous training effects, nutrition, sleep quality, and HRV measurements to tailor training sessions for optimal effectiveness and safety, with adjustable stress areas and durations to match individual goals and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If training instructions are generated based on standard reference values and HRV comparisons, then the method is simple to implement, but the reliability of training recommendations deteriorates because current physical condition and recovery state are not adequately considered
Solution Approach 1:
The system performs preliminary assessment of the user's current physical condition and recovery state before generating training instructions. By evaluating HRV data, recent training history, and recovery metrics in advance, the system prepares personalized baseline data that enables reliable training recommendations without requiring complex real-time analysis during instruction generation
Solution Approach 2:
The system continuously monitors HRV values and compares them against reference values and historical data to provide feedback on the user's current physical condition. This feedback loop allows the system to adjust training recommendations dynamically based on recovery state, ensuring reliability while maintaining implementation simplicity through automated comparison algorithms
2Ease of operation
If fixed stress ranges are defined for training sessions, then the training structure is easy to control, but the adaptability to individual current physical conditions deteriorates
Solution Approach 1:
The system defines multiple pre-established stress ranges with different intensity levels and characteristics. Based on the user's current physical condition assessment, the system dynamically selects and adjusts which stress ranges are appropriate for the current training session, enabling both structural control through predefined ranges and adaptability through condition-based selection
Solution Approach 2:
The system changes key parameters such as stress intensity, duration, and type based on the user's current physical condition. By modifying these parameters within the framework of predefined stress ranges, the system maintains operational controllability while achieving adaptability to individual recovery states and physical conditions
3Productivity
If load intensity is not adjusted based on current recovery state, then the training program is simple to execute, but the training effectiveness deteriorates leading to overtraining or undertraining
Solution Approach 1:
The system performs preliminary assessment of recovery state using HRV measurements and recent training history before executing the training program. This advance preparation enables the selection of appropriate load intensities that match the user's current physical condition, ensuring training effectiveness without requiring complex real-time adjustments during exercise execution
Solution Approach 2:
The system adjusts training parameters including load intensity, duration, and type based on the user's current recovery state. By changing these parameters according to HRV-based recovery assessment, the system maintains simple program execution while achieving reliable training effectiveness and preventing overtraining or undertraining
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Ensures a safe, reliable, and effective training experience by considering individual parameters, preventing overtraining, and optimizing training goals through precise stress range definitions and adjustments, enhancing controllability and variability.
Implementation Method 1
based on the heart rate variability (HRV) recorded on this day
Data Source
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AI summary
The invention relates to a method for generating an instruction for a physical workout, wherein the heart rate variability (HRV) of the person to train is detected and their physical and/or psychological condition (1) is determined in accordance with same, and, based on this condition, an instruction for an upcoming workout is generated for said person, said instruction comprising a recommendation for at least one load range (6') of physical load intensities, which load range (6') is selected, based on the current physical and/or psychological condition, determined on the day of the workout, for the workout on this day, from different load ranges (6) previously individually defined by the person to train, and recommended for training. According to the invention, in order to provide a particularly simple, safe and reliable method, the previously individually defined load ranges (6) are established, in accordance with at least one physical and/or psychological condition determined on a previous day based on the heart rate variability (HRV) detected on this day.