Appropriate physical exercise is beneficial to physical health,but most people blindly carry out high-intensity physical exercise during exercise,which can easily cause physical damage or even endanger life.Therefore,in response to this problem,an exercise fatigue recognition method based on deep learning and ECG signals was proposed in this study.Firstly,the ECG signal of the human body in motion was collected,and the characteristics of the signal were extracted by continuous wavelet transform.Then the extracted ECG features were fused and converted into a two-dimensional image dataset.The VGG neural network with attention mechanism was used to train and recognize the two-dimensional image dataset.Finally,a motion fatigue detection model based on VGG-Attention Mechanism was constructed.The results showed that the motion fatigue detection model constructed by the proposed method has high diagnostic accuracy,with an average accuracy rate of 98.03%,which is of great significance for the development of wearable devices.
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