Abstract
Accurate detection of exercise fatigue based on physiological signals is vital for reason-able 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)com-bined 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.Experi-mental results demonstrate that the proposed fused features outperform other feature combinations significantly.
基金项目
National Natural science Foundation of China(62301056)
Fundamental Research Funds for Central Universities(2022QN005)
NETL
NSTL
万方数据