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. 2022 May 30:2022:5741787.
doi: 10.1155/2022/5741787. eCollection 2022.

The Recognition Method of Athlete Exercise Intensity Based on ECG and PCG

Baiyang Wang  1 , Haiyan Zhu  1
Affiliations

The Recognition Method of Athlete Exercise Intensity Based on ECG and PCG

Baiyang Wang et al. Comput Math Methods Med. .

Abstract

Athletes usually arrange their training plans and determine their training intensity according to the coach's experience and simple physical indicators such as heart rate during exercise. However, the accuracy of this method is poor, and the training plan and exercise intensity arranged according to this method can easily cause physical damage, or the training cannot meet the actual needs. Therefore, in order to realize the reasonable arrangement and monitoring of athletes' training, a method of human exercise intensity recognition based on ECG (electrocardiogram) and PCG (Phonocardiogram) is proposed. First, the ECG and PCG signals are fused into a two-dimensional image, and the dataset is marked and divided according to the different motion intensities. Then, the training set is trained with a CNN (convolutional neural network) to obtain the prediction model of the neural network. Finally, the neural network model is used to identify the ECG and PCG signals to judge the exercise intensity of the athlete, so as to adjust the training plan according to the exercise intensity. The recognition accuracy of the model on the dataset can reach 95.68%. Compared with the use of heart rate to detect the physical state during exercise, ECG records the total potential changes in the process of depolarization and repolarization of the heart, and PCG records the waveform of the beating sound of the heart, which contains richer feature information. Combined with the CNN method, the athlete's exercise intensity prediction model constructed by extracting the features of the athlete's ECG and PCG signals realizes the real-time monitoring of the athlete's exercise intensity and has high accuracy and generalization ability.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
AlexNet network.
Figure 2
Figure 2
Confusion matrix.
Figure 3
Figure 3
Method flow chart.
Figure 4
Figure 4
ECG lead configuration and PCG stethoscope position for dataset.
Figure 5
Figure 5
Six different ECG and PCG fusion signals.
Figure 6
Figure 6
Loss and accuracy.
Figure 7
Figure 7
Data 1 confusion matrix.
Figure 8
Figure 8
Data 1 clustering analysis.
Figure 9
Figure 9
Data 2 loss and accuracy.
Figure 10
Figure 10
Data 2 confusion matrix.
Figure 11
Figure 11
Data 2 clustering analysis.
Figure 12
Figure 12
Comparison of accuracy of exercise intensity classification.
See this image and copyright information in PMC

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