Wearable Device Machine Learning for Aerobic Threshold Prediction
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Solution Overview
Problem
Existing methods for determining aerobic and anaerobic thresholds (AT and AnT) require cumbersome equipment and laboratory settings, making it difficult to obtain personalized fitness guidance for optimal aerobic training zones (OATZ) in everyday exercise routines.
Innovation Solution
The method uses wearable devices to collect anthropometric and physiological data, which are then fed into machine learning models to predict AT and AnT indicators. These predictions are used to define the OATZ, enabling personalized and real-time training guidance on smartwatches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional laboratory methods with cumbersome equipment are used to determine AT and AnT, then measurement precision is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent uses wearable devices to collect physiological data (heart rate, speed, temporal signals) that copy the essential information needed for AT and AnT determination, replacing complex laboratory equipment with simplified sensors that capture the same physiological patterns during natural exercise activities
Solution Approach 2:
The patent replaces mechanical laboratory testing equipment with machine learning models that process temporal signals and physiological data. The ML-based prediction system substitutes physical testing apparatus with computational algorithms that analyze heart rate, speed, and temporal patterns to determine thresholds
2Measurement precision
If traditional laboratory methods are used to obtain AT and AnT, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The wearable device automatically collects physiological data during natural exercise activities without requiring user intervention or laboratory staff assistance. The system self-manages data collection, processing, and threshold determination, providing automated personalized training zones through the smartwatch interface
Solution Approach 2:
The wearable device performs multiple functions: collecting anthropometric data, monitoring physiological signals during exercise, processing temporal patterns, running machine learning predictions, and delivering personalized training guidance. This multi-functional system replaces multiple specialized laboratory equipment and procedures
3Ease of operation
If machine learning models are used to predict AT and AnT from wearable data, then ease of operation is improved, but measurement precision may deteriorate
Solution Approach 1:
The system collects and processes temporal signals and physiological data during natural exercise activities before making predictions. By gathering comprehensive training data in advance and using it to train ML models, the system prepares accurate prediction capabilities that maintain measurement precision while enabling easy operation
Solution Approach 2:
The system continuously monitors physiological signals and compares predicted AT/AnT values with actual performance data. This feedback loop allows the machine learning models to refine their predictions over time, maintaining accuracy while providing real-time guidance through the wearable device interface
Data Source
AI summary
Predict aerobic threshold and anaerobic threshold from data obtained from wearable devices during unsupervised exercise sessions, typical of daily exercise routines. These physiological-based predictions from wearable devices have the potential to improve the general health of a wider audience that may request individualized fitness guidance to train at the OATZ. In short, anthropometric and physiological features, such as gender and heart rate, are collected using wearable devices and used to feed machine learning models to predict the AT and AnT that can be used to define the upper and lower bound, respectively, of the optimal aerobic training zone.