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

作　　者：Xinxin Ma Xinhua Su Huanmin Ge 

机构地区：[1]Schoolof Sports Engineering,Beijing Sport University,Beijing 100084,China

出　　处：《Journal of Beijing Institute of Technology》2024年第4期298-306,共9页北京理工大学学报（英文版）

基　　金：the National Natural Sci-ence Foundation of China(No.62301056);the Fundamental Research Funds for Central Universities(No.2022QN005).

摘　　要：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 arobust capability to reflect cardiovascular information,and their data acquisition devices are quiteconvenient.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 proposedfor PCG multi-class classification.The proposed system enhances the fatigue detection performanceby combining optimized FRFT features with an effective aggregation of linear features and DWTfeatures.The FRFT technique is employed to convert the 1-D PCG signal into 2-D image which issent to a pre-trained convolutional neural network structure,called VGG-16.The features from theVGG-16 were concatenated with the linear and DWT features to form fused features.The fusedfeatures 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 combinationssignificantly.

关 键 词：exercise fatigue phonocardiogram(PCG) fractional Fourier transform(FRFT) dis-crete wavelet transform(DWT) future fusion 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] G804.7[自动化与计算机技术—控制科学与工程]