Title: Detection method of students' online learning state based on posture recognition

Authors: Xiaowei He

Addresses: Computer Engineering Technical College, Guangdong Polytechnic of Science and Technology, Zhuhai, 519090, China

Abstract: Because of the problems of low detection accuracy and long detection time in traditional online learning state detection methods, a new method based on posture recognition is proposed. First of all, a pinhole camera perspective imaging model is constructed, students' online learning images are collected, and the images are processed with greyscale, smoothing, enhancement and light compensation. Secondly, according to the key points of bones, the online learning image features of students after preprocessing are extracted. Finally, identify students' online learning posture, and construct a state detection model combining eye movement behaviour to complete the detection of students' online learning state. The experimental results show that the proposed method has higher accuracy and shorter detection time for students' online learning state detection.

Keywords: posture recognition; online learning status; Gaussian filtering; Laplace operation; bone keys.

DOI: 10.1504/IJBIDM.2024.137730

International Journal of Business Intelligence and Data Mining, 2024 Vol.24 No.3/4, pp.278 - 292

Received: 18 Oct 2022
Accepted: 07 Mar 2023
Published online: 04 Apr 2024 *

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