An Exercise Fatigue Detection Model Based on Machine Learning Methods

TL;DR

This paper presents a machine learning-based exercise fatigue detection model utilizing real-time clinical data and advanced feature extraction techniques, achieving high accuracy in identifying fatigue levels.

Contribution

It introduces a novel feature extraction method combined with an analytical hierarchy process for improved fatigue detection accuracy.

Findings

Detection accuracy reached 98.65%.

Effective real-time fatigue monitoring demonstrated.

Enhanced feature analysis improves detection performance.

Abstract

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%.