Abstract
Aiming at the problems of low accuracy of recognition results, long recognition time, and easy interference in traditional methods, a deep learning-oriented recognition modeling method of college students' psychological stress indicators is proposed. First, the ECG signal is collected by the ECG signal acquisition system, and the wavelet transform method is used to denoise the collected ECG signal. Then, the sequential backward selection algorithm is used to select the features of psychological stress indicators to reduce the feature dimension. Finally, based on the convolutional neural network in deep learning technology, a mental pressure indicator recognition model is established and the model parameters are optimized to realize the recognition of college students’ mental pressure indicators. Experimental results show that the method in this paper has high recognition accuracy, has high recognition efficiency, is not susceptible to interference, and has certain feasibility and effectiveness.
Highlights
People in modern society are exposed to various pressures, and often need to bear pressures from work, life, economy, interpersonal relations, and so on [1, 2]
Method of this article Reference [8] method Reference [9] method method is relatively short, because the ECG signal is preprocessed before index recognition, which reduces the negative impact of interference signal and reduces the recognition time of this method
The recognition result of this method has high accuracy, has high recognition efficiency, and is not easy to disturb, which shows that the recognition result of this method for college students’ psychological stress index is stable and reliable
Summary
People in modern society are exposed to various pressures, and often need to bear pressures from work, life, economy, interpersonal relations, and so on [1, 2]. A pressure identification algorithm based on improved particle swarm optimization BP neural network is proposed in reference [8]. Reference [9] proposes a psychological index recognition modeling method based on social media data, summarizes its feasibility in psychological measurement, introduces feature extraction methods, common machine learning algorithms, and application scenarios, and summarizes and respects the advantages and disadvantages of psychological index recognition modeling. The method of identifying and modeling mental indicators based on social media has limitations in terms of learning costs and hardware costs. It can be seen that it is necessary to conduct research in this area In this context, this article takes college students as the research object and proposes a deep learning-oriented method for identifying and modeling the psychological stress indicators of college students, aiming to improve the effect of identifying psychological stress indicators. The convolution neural network in deep learning technology is used to establish the identification model of college students’ psychological stress indicators
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