Name
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%. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Machine Learning | Frequency domain,Time domain,Approximate entropy,Sample entropy,Feature extraction,Detrended fluctuation analysis,Artificial intelligence,Machine learning,Mathematics,Analytic hierarchy process |
DocType | Volume | Citations |
Journal | abs/1803.07952 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming-Yen Wu | 1 | 0 | 0.34 |
| Chi-Hua Chen | 2 | 66 | 18.92 |
| Chi-Chun Lo | 3 | 593 | 54.99 |