Key result
Wearable ECG devices using 1D-CNN and SVM classifiers accurately predict exercise intensity.
Why the study?
Achieving lightweight, portable, and high-accuracy quantitative physical fitness monitoring for evaluating physical education quality remains a major challenge due to complex equipment and low accuracy of existing methods.
A 1D-CNN and SVM-based algorithm using wearable ECG data can effectively predict exercise intensity for physical education evaluation.
Feasibility shown for wearable AI exercise intensity prediction; leaves open clinical validation before adoption.
As an important part of the education system, college physical education directly affects the comprehensive development of college students' physical and mental quality. It is necessary to build a scientific and efficient evaluation index of college physical education teaching quality. Physical fitness monitoring is an important indicator for the quality evaluation of physical education. However, how to achieve lightweight, portable, and high-accuracy quantitative physical fitness monitoring is currently a major challenge. In order to solve the above challenges, this paper proposes a method of constructing a physical education quality evaluation index based on wearable devices. The wearable device collects human ECG signals, calculates the exercise intensity of participating students, and realizes quantitative evaluation of the quality of physical education teaching. Aiming at the problems of complex equipment and low accuracy of the existing exercise intensity detection methods, this paper proposes an ECG signal wave group detection algorithm based on a one-dimensional convolutional neural network (1D-CNN) to obtain the heart rate variability signal more accurately. After obtaining the ECG feature vector, the SVM classifier is used to predict the exercise intensity. In order to verify the effectiveness of the method in this paper, the real data collected from students of one university and a public available dataset are selected for experiments. The experimental results show that the method proposed in this paper achieves a good performance.
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Lei Fang (2022) studied Physical education quality evaluation. Wearable device with 1D-CNN and SVM classifier vs. Existing exercise intensity detection methods was evaluated on Exercise intensity prediction performance. A wearable device-based method using a 1D-CNN for ECG signal detection and an SVM classifier achieved good performance in predicting exercise intensity for physical education quality evaluation.
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