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

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.

Research on Multimodal Fusion Recognition Method of Upper Limb Motion Patterns

“…Through a large number of mathematical model processing and statistical analysis, some researchers found that there is an exponential decay relationship between heart rate(HR) and heart rate variability(HRV) [18]. Studies have proved that the power spectrum of heart rate variability is affected by exercise intensity [19]. Both heart rate and heart rate variability come from the electrical signals of the heart, which indicated that the electrical signals contain movement information.…”

Computational Methods for Physiological Signal Processing and Data Analysis

“…The physiological signals are useful for monitoring and assessing the physical status of athletes. Wang and Zhu carried out an interesting research work on monitoring and categorizing the body exercise intensity conditions by means of electrocardiogram (ECG) and phonocardiogram (PCG) signal analysis [ 9 ]. The ECG and PCG signals were first projected onto an image with various motion intensity annotations.…”

“…The ECG and PCG signals were first projected onto an image with various motion intensity annotations. The AlexNet CNN architecture that contained five conventional layers, one pooling layer, and three fully connected layers was employed to perform the classifications of body exercise intensities of athletes [ 9 ]. With the purpose of visualizing the cluster scatters, the t-SNE technique was used to reduce the data dimensions in the fully connected layers of the AlexNet architecture.…”

“…With the purpose of visualizing the cluster scatters, the t-SNE technique was used to reduce the data dimensions in the fully connected layers of the AlexNet architecture. The exercise intensity patterns include the human activities during bicycle, treadmill, stationary bicycle, walking at constant speed, laying on bed, and sitting on armchair [ 9 ]. The classification results indicated that the AlexNet was able to provide the highest overall accuracy (95.7%) for six types of exercise intensity [ 9 ], which was superior to the results reported in the previous related works.…”