PCG-Based Exercise Fatigue Detection Method Using FRFT-Based Fusion Model

Accurate detection of exercise fatigue based on physiological signals is vital for reasonable physical activity. As a non-invasive technology, phonocardiogram (PCG) signals possess a robust capability to reflect cardiovascular information, and their data acquisition devices are quite convenient. In this study, a novel hybrid approach of fractional Fourier transform (FRFT) combined with linear and discrete wavelet transform (DWT) features extracted from PCG is proposed for PCG multi-class classification. The proposed system enhances the fatigue detection performance by combining optimized FRFT features with an effective aggregation of linear features and DWT features. The FRFT technique is employed to convert the 1-D PCG signal into 2-D image which is sent to a pre-trained convolutional neural network structure, called VGG-16. The features from the VGG-16 were concatenated with the linear and DWT features to form fused features. The fused features are sent to support vector machine (SVM) to distinguish six distinct fatigue levels. Experimental results demonstrate that the proposed fused features outperform other feature combinations significantly.