Exercise Data Estimation Device for Accurate MAP
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Solution Overview
Problem
Current methods lack accuracy in estimating Maximum Aerobic Power (MAP) for athletes, which is crucial for improving physical ability.
Innovation Solution
An exercise data estimation method and device that obtain exercise heart rate and power data, calculate a heart rate ratio, and update a power estimation model based on predetermined conditions to estimate MAP.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to estimate Maximum Aerobic Power (MAP), then the estimation process is simple, but the accuracy of MAP estimation is insufficient
Solution Approach 1:
The system performs preliminary actions by collecting and storing exercise data (heart rate, power, cadence) over multiple time points before MAP estimation. Validation rules are pre-established to screen data quality, and a power estimation model is pre-trained with valid data points, enabling accurate real-time MAP estimation without complex real-time processing
Solution Approach 2:
The system implements feedback mechanisms by continuously validating exercise data against predetermined conditions (e.g., heart rate-power relationship consistency, cadence ranges). Invalid data points are rejected and excluded from model training, while valid data feeds back to refine the power estimation model, progressively improving MAP estimation accuracy through iterative optimization
2Measurement precision
If all exercise data is used for model training, then the training process is efficient, but the model accuracy deteriorates due to invalid data
Solution Approach 1:
The system extracts and isolates valid data points from the complete exercise dataset by applying validation rules. Specifically, it separates data meeting predetermined conditions (e.g., consistent heart rate-power relationships, appropriate cadence ranges) from invalid data, using only the extracted valid portions for power estimation model training to maintain high accuracy while managing processing efficiency
3Reliability
If the power estimation model is updated continuously with all data, then the model adapts quickly, but the model reliability decreases due to noisy data
Solution Approach 1:
The system implements feedback mechanisms by continuously validating exercise data against predetermined conditions (e.g., heart rate-power relationship consistency, cadence ranges). Invalid data points are rejected and excluded from model training, while valid data feeds back to refine the power estimation model, progressively improving MAP estimation accuracy through iterative optimization
Solution Approach 2:
The system performs preliminary validation and filtering of exercise data before updating the power estimation model. By pre-screening data quality against established criteria and preparing only valid data points for model updates, the system ensures reliable model adaptation without being compromised by noisy or invalid measurements
Data Source
AI summary
An embodiment of this disclosure provides an exercise data estimation method, device, and a computer-readable storage medium. The method includes that an exercise data set corresponding to a tth time point is obtained, where the exercise data set corresponding to the tth time point includes an exercise heart rate and an exercise power corresponding to the tth time point; a heart rate ratio corresponding to the tth time point is determined based on the exercise heart rate corresponding to the tth time point; in response to determining that the exercise data set and the heart rate ratio corresponding to the tth time point match multiple predetermined conditions, the exercise data set is determined to be a valid exercise data set, and a power estimation model is updated based on the exercise data set and the heart rate ratio corresponding to the tth time point; and a maximum aerobic power corresponding to the tth time point is estimated based on the power estimation model.