Addressing Objective and Subjective Indicators of Comfort in Educational Environments

Gómez-Carmona, Oihane; Navarro, Joan; Casado-Mansilla, Diego; López‐de‐Ipiña, Diego; Solé-Beteta, Xavier; Zaballos, Agustín

doi:10.23919/splitech55088.2022.9854272

Cited by 1 publication

(1 citation statement)

References 20 publications

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“…Research has explored persuasive technologies in relation to best workplace practices. This includes taking regular breaks (Jafarinaimi et al, 2005 ; Ludden and Meekhof, 2016 ; Ren et al, 2019 ), fitness apps (Mohadis et al, 2016 ; Ahtinen et al, 2017 ; Paay et al, 2022 ), feedback systems and wearable devices (Bootsman et al, 2019 ; Jiang et al, 2021 ), workstation movement (Min et al, 2015 ; Damen et al, 2020a , b ), chair, desk, and monitor height adjustments (Kronenberg and Kuflik, 2019 ; Kronenberg et al, 2022 ), posture correction (Min et al, 2015 ; Bootsman et al, 2019 ; Kim M. T. et al, 2019 ), mouse/keyboard use and reduction of glare and reflection (Bailly et al, 2016 ), and other healthy work behaviors (Berque et al, 2011 ; Mateevitsi et al, 2014 ; Gomez-Carmona and Casado-Mansilla, 2017 ; Jiang et al, 2021 ; Brombacher et al, 2023 ; Haliburton et al, 2023 ; Robledo Yamamoto et al, 2023 ).…”

Section: Related Workmentioning

confidence: 99%

Toward the design of persuasive systems for a healthy workplace: a real-time posture detection

Ataguba,

Orji

2024

Front. Big Data

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Persuasive technologies, in connection with human factor engineering requirements for healthy workplaces, have played a significant role in ensuring a change in human behavior. Healthy workplaces suggest different best practices applicable to body posture, proximity to the computer system, movement, lighting conditions, computer system layout, and other significant psychological and cognitive aspects. Most importantly, body posture suggests how users should sit or stand in workplaces in line with best and healthy practices. In this study, we developed two study phases (pilot and main) using two deep learning models: convolutional neural networks (CNN) and Yolo-V3. To train the two models, we collected posture datasets from creative common license YouTube videos and Kaggle. We classified the dataset into comfortable and uncomfortable postures. Results show that our YOLO-V3 model outperformed CNN model with a mean average precision of 92%. Based on this finding, we recommend that YOLO-V3 model be integrated in the design of persuasive technologies for a healthy workplace. Additionally, we provide future implications for integrating proximity detection taking into consideration the ideal number of centimeters users should maintain in a healthy workplace.

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