Classification of college students' behavior state based on human physiological information

Xu Yang; Mengle Chen; Qi Zhao; Xiaofeng An

doi:10.1117/12.2685599

28 July 2023 Classification of college students' behavior state based on human physiological information

Xu Yang, Mengle Chen, Qi Zhao, Xiaofeng An

Proceedings Volume 12716, Third International Conference on Digital Signal and Computer Communications (DSCC 2023); 127161N (2023) https://doi.org/10.1117/12.2685599
Event: Third International Conference on Digital Signal and Computer Communications (DSCC 2023), 2023, Xi'an, China

Abstract

AI education uses and continues to use AI and cognitive science technology to try to understand the essence of learning and teaching, thus establishing a system to help students master new skills or understand new concepts. Facing the huge and complex education database, how to use ai technology to help teachers monitor students' classroom behavior and non-classroom behavior in real time without affecting students' daily learning and life, so as to improve the learning efficiency and reduce the effect of failing classes, has attracted the attention and consideration of scholars and experts at home and abroad. This paper proposes a behavior state classification system for college students based on human physiological information. The system uses intelligent bracelets to collect students' physiological information data, conduct large-scale data preprocessing and feature extraction, and construct a multi-classifier model based on combination strategy to realize the classification of college students' learning, entertainment, and sleep. The experimental results show that the recognition accuracy of the system reaches 95.43%.

Citation Download Citation

Xu Yang, Mengle Chen, Qi Zhao, and Xiaofeng An "Classification of college students' behavior state based on human physiological information", Proc. SPIE 12716, Third International Conference on Digital Signal and Computer Communications (DSCC 2023), 127161N (28 July 2023); https://doi.org/10.1117/12.2685599

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KEYWORDS

Data modeling

Machine learning

Body temperature

Data acquisition

Heart

Education and training

Blood pressure

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