An Examination of Mental Stress in College Students: Utilizing Intelligent Perception Data and the Mental Stress Scale

Zhixuan Liao, Xiaomao Fan, Wenjun Ma () and Yingshan Shen ()
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Zhixuan Liao: College of Computer Science, South China Normal University, Guangzhou 510631, China
Xiaomao Fan: College of Big Data and the Internet, Shenzhen Technology University, Shenzhen 518118, China
Wenjun Ma: College of Computer Science, South China Normal University, Guangzhou 510631, China
Yingshan Shen: College of Computer Science, South China Normal University, Guangzhou 510631, China

Mathematics, 2024, vol. 12, issue 10, 1-14

Abstract: In order to solve the problems of traditional mental stress detection in college students that are time-consuming, random, and subjective, this paper proposes an intelligent perception-driven mental stress assessment method for college students. First, we analyze the factors in SRQ and SCL-90, which can be measured by intelligent sensing methods, including sleep, exercise, social interaction, and environment, and then perform feature extraction. Secondly, we use machine learning methods to build a mental stress assessment model. The Shapley additive explanations (SHAP) model is used to explain the training results. Experimental results show that the model proposed in this article can effectively assess the mental stress state of college students. This means that the collection of intelligent perception data based on the mental stress scale can effectively evaluate the mental stress state of college students and provide a new research idea for further developing a non-intrusive and real-time mental stress assessment for college students.

Keywords: stress assessment; stress theory; intelligent perception; data driven; explainability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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