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 cardiovas...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.展开更多
Moderate exercise contributes to health, but excessive exercise may lead to physicalinjury or even endanger life. It is pressing for a device that can detect the intensity of exercise.Therefore, in order to enable rea...Moderate exercise contributes to health, but excessive exercise may lead to physicalinjury 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 harmfrom 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 forsubsequent exercise intensity detection. To enhance HRV analysis, we propose an R-peak detectorbased on encoder-decoder and temporal convolutional network (TCN). Experimental resultsdemonstrate 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 extractionin 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 intensitydetection system achieves 90.2% specificity, 96.7% recall, and 98.1% accuracy in five-fold cross-validation.展开更多
基于EBE-PCG(element by element-preconditioned conjugate gradient)策略的并行算法不用形成总体刚度矩阵,而且无需进行三维模型的区域分解,从而提高了并行计算的速度和效率,是实现协同优化设计的性能函数快速分析技术的有效途径。文...基于EBE-PCG(element by element-preconditioned conjugate gradient)策略的并行算法不用形成总体刚度矩阵,而且无需进行三维模型的区域分解,从而提高了并行计算的速度和效率,是实现协同优化设计的性能函数快速分析技术的有效途径。文中详细介绍有限元EBE(element by element)的运算方法,给出EBE-PCG并行算法的实现步骤,最后在网络集群环境下,综合运用多种编程语言和分析工具,实现基于EBE-PCG策略的三维有限元并行计算。计算结果表明,该并行算法的计算误差小,并行效率高,适合于性能函数的快速求解。展开更多
基金the National Natural Sci-ence Foundation of China(No.62301056)the Fundamental Research Funds for Central Universities(No.2022QN005).
文摘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.
基金the National Natural Science Foundation of China(No.62301056)the Fundamental Research Funds for Central Universities(No.2022QN005).
文摘Moderate exercise contributes to health, but excessive exercise may lead to physicalinjury 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 harmfrom 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 forsubsequent exercise intensity detection. To enhance HRV analysis, we propose an R-peak detectorbased on encoder-decoder and temporal convolutional network (TCN). Experimental resultsdemonstrate 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 extractionin 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 intensitydetection system achieves 90.2% specificity, 96.7% recall, and 98.1% accuracy in five-fold cross-validation.
文摘基于EBE-PCG(element by element-preconditioned conjugate gradient)策略的并行算法不用形成总体刚度矩阵,而且无需进行三维模型的区域分解,从而提高了并行计算的速度和效率,是实现协同优化设计的性能函数快速分析技术的有效途径。文中详细介绍有限元EBE(element by element)的运算方法,给出EBE-PCG并行算法的实现步骤,最后在网络集群环境下,综合运用多种编程语言和分析工具,实现基于EBE-PCG策略的三维有限元并行计算。计算结果表明,该并行算法的计算误差小,并行效率高,适合于性能函数的快速求解。
