An Exercise Fatigue Detection Model Based on Machine Learning Methods

7 Mar 2018  ·  Ming-Yen Wu, Chi-Hua Chen, Chi-Chun Lo ·

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%. read more

PDF Abstract
No code implementations yet. Submit your code now



  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here