Zhu Haiyan, Ji Yuelong, Wang Baiyang, Kang Yuyun
School of Physical Education and Health, Linyi University, Linyi, China.
School of Information Science and Engineering, Linyi University, Linyi, China.
Front Physiol. 2022 Aug 30;13:965974. doi: 10.3389/fphys.2022.965974. eCollection 2022.
Reasonable exercise is beneficial to human health. However, it is difficult for ordinary athletes to judge whether they are already in a state of fatigue that is not suitable for exercise. In this case, it is easy to cause physical damage or even life-threatening. Therefore, to health sports, protecting the human body in sports not be injured by unreasonable sports, this study proposes an exercise fatigue diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN). The method analyzes and diagnoses the real-time electrocardiogram, and obtains whether the current exerciser has exercise fatigue according to the electrocardiogram. The algorithm first performs short-time Fourier transform on the electrocardiogram (ECG) signal to obtain the time spectrum of the signal, which is divided into training set and validation set. The training set is then fed into the convolutional neural network for learning, and the network parameters are adjusted. Finally, the trained convolutional neural network model is applied to the test set, and the recognition result of fatigue level is output. The validity and feasibility of the method are verified by the ECG experiment of exercise fatigue degree. The experimental recognition accuracy rate can reach 97.70%, which proves that the constructed sports fatigue diagnosis model has high diagnostic accuracy and is feasible for practical application.
合理运动对人体健康有益。然而，普通运动员很难判断自己是否已经处于不适宜运动的疲劳状态。在这种情况下，很容易造成身体损伤甚至危及生命。因此，为了实现健康运动，保护人体在运动中不受到不合理运动的伤害，本研究提出了一种基于短时傅里叶变换（STFT）和卷积神经网络（CNN）的运动疲劳诊断方法。该方法对实时心电图进行分析诊断，并根据心电图判断当前运动者是否存在运动疲劳。算法首先对心电图（ECG）信号进行短时傅里叶变换，得到信号的时频谱，将其分为训练集和验证集。然后将训练集输入卷积神经网络进行学习，调整网络参数。最后，将训练好的卷积神经网络模型应用于测试集，输出疲劳等级的识别结果。通过运动疲劳程度的心电图实验验证了该方法的有效性和可行性。实验识别准确率可达97.70%，证明所构建的运动疲劳诊断模型具有较高的诊断准确率，在实际应用中是可行的。
