An Exercise Fatigue Detection Model Based on Machine Learning Methods
This work addresses exercise fatigue detection for athletes or clinical applications, but it appears incremental as it builds on existing methods with minor improvements.
The study tackled exercise fatigue detection by proposing a model that combines feature extraction methods with machine learning algorithms, achieving an accuracy of 98.65% in detecting fatigue levels.
This study proposes an exercise fatigue detection model based on real-time clinical data which includes time domain analysis, frequency domain analysis, detrended fluctuation analysis, approximate entropy, and sample entropy. Furthermore, this study proposed a feature extraction method which is combined with an analytical hierarchy process to analyze and extract critical features. Finally, machine learning algorithms were adopted to analyze the data of each feature for the detection of exercise fatigue. The practical experimental results showed that the proposed exercise fatigue detection model and feature extraction method could precisely detect the level of exercise fatigue, and the accuracy of exercise fatigue detection could be improved up to 98.65%.