Article: Dynamic evaluation of college students' psychological state based on multimodal physiological signal fusion and deep generation model Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.17 pp.133 - 146 Abstract: The dynamic assessment of the psychological state of college students is an important research direction in mental health management. In response to the problem of insufficient capture of psychological state changes by existing methods, this paper proposes a dynamic assessment method that combines multimodal physiological signal fusion and deep generation models. Firstly, collect multimodal physiological data and eliminate noise through timing synchronisation and data pre-processing techniques. Secondly, utilising a multimodal feature extraction network based on transformer structure to achieve feature fusion of physiological signals. Subsequently, an improved variational autoencoder (VAE) was designed, combined with an LSTM model, to predict the trend of psychological state changes. Technical support for real-time monitoring and tailored intervention of college students' mental health status is provided by the experimental results showing that the suggested method outperforms current methods in terms of accuracy in psychological state classification and dynamic prediction performance. Inderscience Publishers - linking academia, business and industry through research

Title: Dynamic evaluation of college students' psychological state based on multimodal physiological signal fusion and deep generation model

Authors: Jing Li

Addresses: Psychologically Healthy Education Center, Inner Mongolia University of Finance and Economics, Huhehot 010000, China

Abstract: The dynamic assessment of the psychological state of college students is an important research direction in mental health management. In response to the problem of insufficient capture of psychological state changes by existing methods, this paper proposes a dynamic assessment method that combines multimodal physiological signal fusion and deep generation models. Firstly, collect multimodal physiological data and eliminate noise through timing synchronisation and data pre-processing techniques. Secondly, utilising a multimodal feature extraction network based on transformer structure to achieve feature fusion of physiological signals. Subsequently, an improved variational autoencoder (VAE) was designed, combined with an LSTM model, to predict the trend of psychological state changes. Technical support for real-time monitoring and tailored intervention of college students' mental health status is provided by the experimental results showing that the suggested method outperforms current methods in terms of accuracy in psychological state classification and dynamic prediction performance.

Keywords: multimodal physiological signals; dynamic assessment of psychological state; deep generative model; feature fusion; variational autoencoder; VAE.

DOI: 10.1504/IJICT.2025.146669

International Journal of Information and Communication Technology, 2025 Vol.26 No.17, pp.133 - 146

Received: 20 Mar 2025
Accepted: 11 Apr 2025
Published online: 11 Jun 2025 *

Title: Dynamic evaluation of college students' psychological state based on multimodal physiological signal fusion and deep generation model

Keep up-to-date