Year: 2025
Type: conference-paper
Abstract: The widespread adoption of wearable smart devices has increased expectations for the development of personalized learning environments. These environments offer real-time analysis of learners' study states by utilizing sensor data from wearable devices and tablets, as well as facial image data captured during learning sessions. In this study, we propose a smart learning environment that integrates multiple smart devices, with a particular focus on detecting and analyzing learners' facial images recorded by the front camera of a tablet used as a learning terminal. Facial image analysis was performed using the InsightFace library, which accurately extracts facial landmarks from angled facial images. The results highlight the feasibility of capturing learners' facial orientations using this monitoring function and explore how these data can contribute to enhancing smart learning environments.
Author: Masashi Katsumata
Institution: Nippon Institute of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4414420367