Mental fatigue state recognition method based on convolution neural network and long short-term memory

现代生活节奏加快，生活压力逐渐增大，长期累积的心理疲劳对健康构成威胁。通过分析生理信号和参数，本文提出一种可以识别心理疲劳状态的方法，从而有助于维护健康生活。本文所提方法是基于卷积神经网络与长短时记忆网络结合的心电信号心理疲劳状态识别方法。首先，利用一维卷积神经网络模型的卷积层提取局部特征，通过池化层提取关键信息，同时去除部分冗余数据。然后，将提取的特征作为长短时记忆网络模型的输入，以进一步进行心电特征的融合。最后，通过全连接层整合关键信息，成功实现了对心理疲劳状态的准确识别。研究结果表明，相较于传统的机器学习算法，本文提出的方法显著提高了心理疲劳识别的准确性，识别的准确度达到了96.3%，可为心理疲劳的预警和评估提供可靠的基础。.; The pace of modern life is accelerating, the pressure of life is gradually increasing, and the long-term accumulation of mental fatigue poses a threat to health. By analyzing physiological signals and parameters, this paper proposes a method that can identify the state of mental fatigue, which helps to maintain a healthy life. The method proposed in this paper is a new recognition method of psychological fatigue state of electrocardiogram signals based on convolutional neural network and long short-term memory. Firstly, the convolution layer of one-dimensional convolutional neural network model is used to extract local features, the key information is extracted through pooling layer, and some redundant data is removed. Then, the extracted features are used as input to the long short-term memory model to further fuse the ECG features. Finally, by integrating the key information through the full connection layer, the accurate recognition of mental fatigue state is successfully realized. The results show that compared with traditional machine learning algorithms, the proposed method significantly improves the accuracy of mental fatigue recognition to 96.3%, which provides a reliable basis for the early warning and evaluation of mental fatigue.