Article: An affective-behavioural fusion framework for proactive student mental health monitoring Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.50 pp.88 - 112 Abstract: With the increasing prominence of psychological health problems of students in colleges and universities, efficient and accurate identification of psychological states has become an urgent issue. To address this issue, this paper proposes a psychological monitoring system for college students based on affective computing and behavioural trajectory analysis (AffectPath-PM). The system firstly extracts students' multimodal characteristics by using the affective computing module, secondly obtains the characteristics of different patterns through the analysis of behavioural trajectories, and finally real-time monitoring and intervention is achieved by combining comprehensive assessment and early warning feedback. Experimental results indicate that the overall performance of this system demonstrates an average improvement of approximately 4.5% compared to the reference method. Small-scale validation experiments also demonstrate its applicability and scalability. This system offers universities a comprehensive and efficient mental health monitoring solution, possessing significant practical value. Inderscience Publishers - linking academia, business and industry through research

Title: An affective-behavioural fusion framework for proactive student mental health monitoring

Authors: Fu Yao; Zheng Liu

Addresses: Jingchu University of Technology, Jingmen, 448000, China ' Jingchu University of Technology, Jingmen, 448000, China

Abstract: With the increasing prominence of psychological health problems of students in colleges and universities, efficient and accurate identification of psychological states has become an urgent issue. To address this issue, this paper proposes a psychological monitoring system for college students based on affective computing and behavioural trajectory analysis (AffectPath-PM). The system firstly extracts students' multimodal characteristics by using the affective computing module, secondly obtains the characteristics of different patterns through the analysis of behavioural trajectories, and finally real-time monitoring and intervention is achieved by combining comprehensive assessment and early warning feedback. Experimental results indicate that the overall performance of this system demonstrates an average improvement of approximately 4.5% compared to the reference method. Small-scale validation experiments also demonstrate its applicability and scalability. This system offers universities a comprehensive and efficient mental health monitoring solution, possessing significant practical value.

Keywords: affective computing; behavioural trajectory analysis; psychological health monitoring; multimodal data.

DOI: 10.1504/IJICT.2025.151068

International Journal of Information and Communication Technology, 2025 Vol.26 No.50, pp.88 - 112

Received: 21 Oct 2025
Accepted: 15 Nov 2025
Published online: 12 Jan 2026 *

Title: An affective-behavioural fusion framework for proactive student mental health monitoring

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