Citation

Nazaruddin, Mohamad Nazmi Zharfan and Zainal Abidin, Nor Afirdaus and Aminuddin, Raihah and Abu Samah, Khyrina Airin Fariza and Mohamed Ibrahim, Asma Zubaida and Yusoh, Syarifah Diyanah and Abu Mangshor, Nur Nabilah and Mohd Nasir, Siti Diana Nabilah (2023) Utilising the YOLOv3 Algorithm for the Student Posture Recognition System in Classroom Settings. In: 2023 IEEE 8th International Conference on Recent Advances and Innovations in Engineering (ICRAIE), 02-03 December 2023, Kuala Lumpur, Malaysia.

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Abstract

Effective student-teacher interaction helps transfer knowledge, clarify concepts, and create a conductive learning environment. The effectiveness of the interaction can be seen through students’ behaviour and various factors, such as pos- ture and gestures in the classroom. However, educators face significant difficulties in tracking each student’s performance and behaviour during class. Therefore, this study focuses on student posture recognition in classroom settings, which is essential for monitoring student behaviour and engagement during lectures. The proposed system utilises the YOLOv3 machine learning model for real-time detection. A dataset of student postures was collected from Google Images, and the data was used to train a deep neural network. The model was then tested on classroom images and compared to manual annotations. The results showed that the model can accurately recognise student postures with high precision, recall, F1-score, and mean average precision (mAP), achieving an average precision of 88%, recall of 89%, F1-score of 88%, and mAP of 95.20%. The real-time processing capability of YOLOv3 allows for immediate posture detection during lectures in a classroom; this may help educators monitor student behaviour and engagement.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	classroom, YOLOv3, deep learning, posture recognition
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Business (FOB)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	26 Apr 2024 03:31
Last Modified:	26 Apr 2024 03:31
URII:	http://shdl.mmu.edu.my/id/eprint/12377

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