Computer users sitting posture classification using distinct feature points and small scale convolutional neural network for humana computer intelligent interactive system during COVID-19

The sudden change in our workplace practices from face-to-face work to work from home setup due to the pandemic has brought positive and negative impacts on our overall health. In literature, the use of deep learning and specialized cameras in the estimation of the human pose is popular even if ther...

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Main Authors: Estrada, Jheanel, Vea, Larry
Other Authors: Energy Research Institute @ NTU (ERI@N)
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/173618
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1736182024-02-20T15:37:35Z Computer users sitting posture classification using distinct feature points and small scale convolutional neural network for humana computer intelligent interactive system during COVID-19 Estrada, Jheanel Vea, Larry Energy Research Institute @ NTU (ERI@N) Computer and Information Science Human pose estimation Work from home The sudden change in our workplace practices from face-to-face work to work from home setup due to the pandemic has brought positive and negative impacts on our overall health. In literature, the use of deep learning and specialized cameras in the estimation of the human pose is popular even if there is a need for high computational resources and complex models. For this purpose, this study developed an intelligent and interactive system utilizing a human estimation model with the use of distinct keypoint such as thoracic, thoraco lumbar, and lumbar points in the spine. An objective type of a dataset captured in a work from home environment with the knowledge and guidance of Licensed Physical Therapists to assess proper and improper sitting posture was developed. The study developed and implemented a small-scale convolutional network and low-cost smartphone camera to recognize body key points. Once all the feature points' locations were extracted, additional features such as cosine similarity and point distances were calculated. Next, feature selection and optimization were utilized to classify proper and improper sitting postures. As a result, the study developed (2) datasets and (2) models with an accuracy of 85.18 and 92.07% and kappa of 0.691 and 0.838 respectively. Published version 2024-02-19T05:54:22Z 2024-02-19T05:54:22Z 2023 Journal Article Estrada, J. & Vea, L. (2023). Computer users sitting posture classification using distinct feature points and small scale convolutional neural network for humana computer intelligent interactive system during COVID-19. Journal of Computer Science, 19(4), 493-513. https://dx.doi.org/10.3844/jcssp.2023.493.513 1549-3636 https://hdl.handle.net/10356/173618 10.3844/jcssp.2023.493.513 2-s2.0-85153407796 4 19 493 513 en Journal of Computer Science © 2023 Jheanel Estrada and Larry Vea. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Human pose estimation
Work from home
spellingShingle Computer and Information Science
Human pose estimation
Work from home
Estrada, Jheanel
Vea, Larry
Computer users sitting posture classification using distinct feature points and small scale convolutional neural network for humana computer intelligent interactive system during COVID-19
description The sudden change in our workplace practices from face-to-face work to work from home setup due to the pandemic has brought positive and negative impacts on our overall health. In literature, the use of deep learning and specialized cameras in the estimation of the human pose is popular even if there is a need for high computational resources and complex models. For this purpose, this study developed an intelligent and interactive system utilizing a human estimation model with the use of distinct keypoint such as thoracic, thoraco lumbar, and lumbar points in the spine. An objective type of a dataset captured in a work from home environment with the knowledge and guidance of Licensed Physical Therapists to assess proper and improper sitting posture was developed. The study developed and implemented a small-scale convolutional network and low-cost smartphone camera to recognize body key points. Once all the feature points' locations were extracted, additional features such as cosine similarity and point distances were calculated. Next, feature selection and optimization were utilized to classify proper and improper sitting postures. As a result, the study developed (2) datasets and (2) models with an accuracy of 85.18 and 92.07% and kappa of 0.691 and 0.838 respectively.
author2 Energy Research Institute @ NTU (ERI@N)
author_facet Energy Research Institute @ NTU (ERI@N)
Estrada, Jheanel
Vea, Larry
format Article
author Estrada, Jheanel
Vea, Larry
author_sort Estrada, Jheanel
title Computer users sitting posture classification using distinct feature points and small scale convolutional neural network for humana computer intelligent interactive system during COVID-19
title_short Computer users sitting posture classification using distinct feature points and small scale convolutional neural network for humana computer intelligent interactive system during COVID-19
title_full Computer users sitting posture classification using distinct feature points and small scale convolutional neural network for humana computer intelligent interactive system during COVID-19
title_fullStr Computer users sitting posture classification using distinct feature points and small scale convolutional neural network for humana computer intelligent interactive system during COVID-19
title_full_unstemmed Computer users sitting posture classification using distinct feature points and small scale convolutional neural network for humana computer intelligent interactive system during COVID-19
title_sort computer users sitting posture classification using distinct feature points and small scale convolutional neural network for humana computer intelligent interactive system during covid-19
publishDate 2024
url https://hdl.handle.net/10356/173618
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