Sedentary sitting posture recognition with YOLOv3 algorithm / Muhammad Amin Mohamad Razali

This paper discusses the prevalence of sedentary lifestyle and how it affects the sitting behaviour among computer users. Sedentary lifestyle has been adopted due to the increasing usage of computers for study or for work, which has caused an increase in sitting behaviour among computer users. The i...

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Main Author:	Mohamad Razali, Muhammad Amin
Format:	Thesis
Language:	English
Published:	2023
Subjects:	Integer programming
Online Access:	https://ir.uitm.edu.my/id/eprint/89092/1/89092.pdf
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id	my-uitm-ir.89092
record_format	uketd_dc
spelling	my-uitm-ir.890922024-09-29T03:03:01Z Sedentary sitting posture recognition with YOLOv3 algorithm / Muhammad Amin Mohamad Razali 2023 Mohamad Razali, Muhammad Amin Integer programming This paper discusses the prevalence of sedentary lifestyle and how it affects the sitting behaviour among computer users. Sedentary lifestyle has been adopted due to the increasing usage of computers for study or for work, which has caused an increase in sitting behaviour among computer users. The increase in sitting behaviour may lead to longer time spent in an inconsistent and potentially bad sitting postures. Sedentary sitting behaviour has caused numerous problems such as rising health issues concerning back and neck pain. Other than that, it is challenging and can be uncomfortable for computer users to break a particular problematic sitting habit. Existing solutions to sedentary sitting behaviour problems by utilizing pressure and depth sensors requires suffers from lack of calibration and are often expensive. The objective of this paper is to design, develop, and test a sitting posture recognition system using deep learning technique. Therefore, this paper discusses on the usage of deep learning techniques sitting posture recognition and presents the usage of You Only Look Once (YOLO) deep learning model, specifically the YOLOv3 model for this task. The implemented YOLOv3 model has managed to perform sitting posture recognition based on four classes: good head posture, bad head posture, good torso posture, and bad torso posture with Mean Average Precision accuracy of 87.63%. In conclusion, this paper presents a solution to recognize good and bad head and torso sedentary sitting postures and highlight the opportunities such as improving accuracy and detection speed that can be explored to further improve sitting posture recognition for future research. 2023 Thesis https://ir.uitm.edu.my/id/eprint/89092/ https://ir.uitm.edu.my/id/eprint/89092/1/89092.pdf text en public degree Universiti Teknologi MARA, Melaka College of Computing, Informatics and Mathematics
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Integer programming
spellingShingle	Integer programming Mohamad Razali, Muhammad Amin Sedentary sitting posture recognition with YOLOv3 algorithm / Muhammad Amin Mohamad Razali
description	This paper discusses the prevalence of sedentary lifestyle and how it affects the sitting behaviour among computer users. Sedentary lifestyle has been adopted due to the increasing usage of computers for study or for work, which has caused an increase in sitting behaviour among computer users. The increase in sitting behaviour may lead to longer time spent in an inconsistent and potentially bad sitting postures. Sedentary sitting behaviour has caused numerous problems such as rising health issues concerning back and neck pain. Other than that, it is challenging and can be uncomfortable for computer users to break a particular problematic sitting habit. Existing solutions to sedentary sitting behaviour problems by utilizing pressure and depth sensors requires suffers from lack of calibration and are often expensive. The objective of this paper is to design, develop, and test a sitting posture recognition system using deep learning technique. Therefore, this paper discusses on the usage of deep learning techniques sitting posture recognition and presents the usage of You Only Look Once (YOLO) deep learning model, specifically the YOLOv3 model for this task. The implemented YOLOv3 model has managed to perform sitting posture recognition based on four classes: good head posture, bad head posture, good torso posture, and bad torso posture with Mean Average Precision accuracy of 87.63%. In conclusion, this paper presents a solution to recognize good and bad head and torso sedentary sitting postures and highlight the opportunities such as improving accuracy and detection speed that can be explored to further improve sitting posture recognition for future research.
format	Thesis
qualification_level	Bachelor degree
author	Mohamad Razali, Muhammad Amin
author_facet	Mohamad Razali, Muhammad Amin
author_sort	Mohamad Razali, Muhammad Amin
title	Sedentary sitting posture recognition with YOLOv3 algorithm / Muhammad Amin Mohamad Razali
title_short	Sedentary sitting posture recognition with YOLOv3 algorithm / Muhammad Amin Mohamad Razali
title_full	Sedentary sitting posture recognition with YOLOv3 algorithm / Muhammad Amin Mohamad Razali
title_fullStr	Sedentary sitting posture recognition with YOLOv3 algorithm / Muhammad Amin Mohamad Razali
title_full_unstemmed	Sedentary sitting posture recognition with YOLOv3 algorithm / Muhammad Amin Mohamad Razali
title_sort	sedentary sitting posture recognition with yolov3 algorithm / muhammad amin mohamad razali
granting_institution	Universiti Teknologi MARA, Melaka
granting_department	College of Computing, Informatics and Mathematics
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/89092/1/89092.pdf
_version_	1811768734274027520

Sedentary sitting posture recognition with YOLOv3 algorithm / Muhammad Amin Mohamad Razali

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