Exercise Fatigue Monitoring Based on R-Peak Detection Using UNET-TCN

Abstract

Moderate exercise contributes to health, but excessive exercise may lead to physical injury or even endanger life. It is pressing for a device that can detect the intensity of exercise. Therefore, in order to enable real-time detection of exercise intensity and mitigate the risks of harm from excessive exercise, a exercise intensity monitoring system based on the heart rate variability (HRV) from electrocardiogram (ECG) signal and linear features from phonocardiogram (PCG) signal is proposed. The main contributions include: First, accurate analysis of HRV is crucial for subsequent exercise intensity detection. To enhance HRV analysis, we propose an R-peak detector based on encoder-decoder and temporal convolutional network (TCN). Experimental results demonstrate that the proposed R-peak detector achieves an F1 score exceeding 0.99 on real high-intensity exercise ECG datasets. Second, an exercise fatigue monitoring system based on multi-signal feature fusion is proposed. Initially, utilizing the proposed R-peak detector for HRV extraction in exercise intensity detection，which outperforms traditional algorithms, with the system achieving a classification performance of 0.933 sensitivity, 0.802 specificity, and 0.960 accuracy. To further improve the system, we combine HRV with the linear features of PCG. Our exercise intensity detection system achieves 90.2% specificity, 96.7% recall, and 98.1% accuracy in five-fold cross-validation.

Keywords

heart rate variability (HRV) / phonocardiogram (PCG) / Unet / temporal convolutional network (TCN) / machine learning / exercise intensity