Automatically Adjusting Computer Screen

Kronenberg, Rotem; Kuflik, Tsvi

doi:10.1145/3314183.3324980

Cited by 7 publications

(7 citation statements)

References 30 publications

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“…As most oice work today is done while users sits at a desk, we propose a self-adjusting computer screen system for seated users, which changes its position and orientation vis-à-vis the user, as we already suggested in [36]. We believe this is another step in designing an intelligent and user-friendly work environment.…”

Section: Our Proposal For Aligning the Computer Screen Position And A...mentioning

confidence: 99%

Improving Office Workers’ Workspace Using a Self-adjusting Computer Screen

Kronenberg

Kuflik

Shimshoni

2022

ACM Trans. Interact. Intell. Syst.

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With the rapid evolution of technology, computers and their users’ workspaces have become an essential part of our life in general. Today, many people use computers both for work and for personal needs, spending long hours sitting at a desk in front of a computer screen, changing their pose slightly from time to time. This phenomenon impacts people’s health negatively, adversely affecting their musculoskeletal and ocular systems. To mitigate these risks, several different ergonomic solutions have been suggested. This study proposes, demonstrates, and evaluates a technological solution that automatically adjusts the computer screen position and orientation to its user’s current pose, using a simple RGB camera and robotic arm. The automatic adjustment will reduce the physical load on users and better fit their changing poses. The user’s pose is extracted from images continuously acquired by the system’s camera. The most suitable screen position is calculated according to the user’s pose and ergonomic guidelines. Thereafter, the robotic arm adjusts the screen accordingly. The evaluation was done through a user study with 35 users who rated both the idea and the prototype system itself highly.

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Section: Our Proposal For Aligning the Computer Screen Position And A...mentioning

confidence: 99%

Improving Office Workers’ Workspace Using a Self-adjusting Computer Screen

Kronenberg

Kuflik

Shimshoni

2022

ACM Trans. Interact. Intell. Syst.

Self Cite

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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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“…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: Persuasive Technologies and The Workplacementioning

confidence: 99%

“…Some significant research studies have delved into the application of machine learning in the realm of workplace practices. These studies have particularly focused on classifying healthy and active work styles (Rabbi et al, 2015) and automatic adjustments of chair and desk heights (Kronenberg and Kuflik, 2019). In their study, Kronenberg and Kuflik (2019) proposed a deep learning design for robotic arms that are capable of adjusting chair and desk heights based on body positions.…”

Section: Machine Learning and Workplace Practicesmentioning

confidence: 99%

“…These studies have particularly focused on classifying healthy and active work styles (Rabbi et al, 2015) and automatic adjustments of chair and desk heights (Kronenberg and Kuflik, 2019). In their study, Kronenberg and Kuflik (2019) proposed a deep learning design for robotic arms that are capable of adjusting chair and desk heights based on body positions. Although the system was still in the implementation stage, initial results demonstrated the potential of embedding a camera in a robotic arm.…”

Automatically Adjusting Computer Screen

Cited by 7 publications

References 30 publications

Improving Office Workers’ Workspace Using a Self-adjusting Computer Screen

Improving Office Workers’ Workspace Using a Self-adjusting Computer Screen

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

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