research-article

Open access

Ubiquitous Machinery Monitoring - A Field Study on Manufacturing Workers' User Experience of Mobile and Wearable Monitoring Apps

Authors:

Sebastian Müller,

Matthias Baldauf,

Arne SeeligerAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 6, Issue MHCI

Article No.: 198, Pages 1 - 22

https://doi.org/10.1145/3546733

Published: 20 September 2022 Publication History

Abstract

Fueled by ongoing digitization efforts, manufacturing is currently undergoing a transformational process towards interconnected machinery and workforce, which enables a wide range of interactive monitoring and controlling applications. Whereas existing user-centered work addressed remote monitoring from office workplaces, it remains unclear how manufacturing workers experience and adopt machinery monitoring apps on mobile and wearable devices. To close this gap, we conducted a four-week field study in a running factory to study workers' overall user experience and acceptance of such monitoring apps, the subjective impact on their work routines, and their preferred device type. Under productive operation, 11 manufacturing workers used functional application prototypes on smartphones and smartwatches to receive notifications of machine incidents. In 22 individual interviews and two focus groups, we collected the participants' impressions and assessments. Based on these results, we derive a set of recommendations for designing and deploying machinery monitoring apps for manufacturing workers.

References

[1]

Mario Aehnelt and Bodo Urban. 2014. Follow-Me: Smartwatch Assistance on the Shop Floor. In HCI in Business, Fiona Fui-Hoon Nah (Ed.). Springer International Publishing, Cham, 279--287.

[2]

Susanna Aromaa, Iina Aaltonen, Eija Kaasinen, Joona Elo, and Ilari Parkkinen. 2016. Use of wearable and augmented reality technologies in industrial maintenance work. In AcademicMindtrek 2016 - Proceedings of the 20th International Academic Mindtrek Conference. 235--242. https://doi.org/10.1145/2994310.2994321

Digital Library

[3]

Matthias Baldauf, Sebastian Müller, Arne Seeliger, Tobias Küng, Andreas Michel, and Werner Züllig. 2021. Human Interventions in the Smart Factory -- A Case Study on Co-Designing Mobile and Wearable Monitoring Systems with Manufacturing Staff. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1--6. https://doi.org/10.1145/3411763.3451774

Digital Library

[4]

Virginia Braun and Victoria Clarke. 2012. Thematic analysis. In APA handbook of research methods in psychology, Vol 2: Research designs: Quantitative, qualitative, neuropsychological, and biological. American Psychological Association, 57--71. https://doi.org/10.1037/13620-004

[5]

John Brooke. 1995. SUS: A quick and dirty usability scale. Usability Eval. Ind. 189 (11 1995).

[6]

Andre Bröring, Arno Fast, Sebastian Büttner, Mario Heinz, and Carsten Röcker. 2019. Smartwatches zur Unterstützung von Produktionsmitarbeitern. (2019). https://doi.org/10.18420/MUC2019-UP-0324

[7]

Julia N. Czerniak, Nikolas Schierhorst, Christopher Brandl, Alexander Mertens, and Verena Nitsch. 2020. Smart digital assistance devices for the support of machine operation processes at future production workplaces. Advances in Intelligent Systems and Computing 1217 AISC (2020), 491--497. https://doi.org/10.1007/978--3-030--51828--8_64

[8]

Rodrigo De Oliveira, Mauro Cherubini, and Nuria Oliver. 2010. MoviPill: Improving medication compliance for elders using a mobile persuasive social game. Proceedings of the 2010 ACM Conference on Ubiquitous Computing (2010), 251--260. https://doi.org/10.1145/1864349.1864371

Digital Library

[9]

Joel E. Fischer, Chris Greenhalgh, and Steve Benford. 2011. Investigating episodes of mobile phone activity as indicators of opportune moments to deliver notifications. 13th International Conference on Human-Computer Interaction with Mobile Devices and Services (2011), 181--190. https://doi.org/10.1145/2037373.2037402

Digital Library

[10]

Markus Funk, Juana Heusler, Elif Akcay, Klaus Weiland, and Albrecht Schmidt. 2016. Haptic, auditory, or visual? Towards optimal error feedback at manual assembly workplaces. ACM International Conference Proceeding Series 29-June-20 (2016), 1--6. https://doi.org/10.1145/2910674.2910683

Digital Library

[11]

Google. 2021. Principles of Wear OS development. https://developer.android.com/training/wearables/principles. Accessed: 2022-02-01.

[12]

Jérôme Jetter, Jörgen Eimecke, and Alexandra Rese. 2018. Augmented reality tools for industrial applications: What are potential key performance indicators and who benefits? Computers in Human Behavior 87, February (2018), 18--33. https://doi.org/10.1016/j.chb.2018.04.054

Digital Library

[13]

David Richard Johnson and James C. Creech. 1983. Ordinal Measures in Multiple Indicator Models: A Simulation Study of Categorization Error. American Sociological Review 48, 3 (June 1983), 398. https://doi.org/10.2307/2095231

[14]

Xiang T.R. Kong, Hao Luo, George Q. Huang, and Xuan Yang. 2019. Industrial wearable system: the human-centric empowering technology in Industry 4.0. Journal of Intelligent Manufacturing 30, 8 (2019), 2853--2869. https://doi.org/ 10.1007/s10845-018--1416--9

[15]

Christian Krupitzer, Sebastian Müller, Veronika Lesch, Marwin Züfle, Janick Edinger, Alexander Lemken, Dominik Schäfer, Samuel Kounev, and Christian Becker. 2020. A Survey on Human Machine Interaction in Industry 4.0. (2020), 45 pages. http://arxiv.org/abs/2002.01025

[16]

Francesco Longo, Letizia Nicoletti, and Antonio Padovano. 2017. Smart operators in industry 4.0: A human-centered approach to enhance operators' capabilities and competencies within the new smart factory context. Computers and Industrial Engineering 113 (2017), 144--159. https://doi.org/10.1016/j.cie.2017.09.016

Digital Library

[17]

Sebastian Mach, Almut Kastrau, and Franziska Schmalfuß. 2018. Information at hand -- Using wearable devices to display task information in the context of industry 4.0. Communications in Computer and Information Science 850, June 2019 (2018), 93--100. https://doi.org/10.1007/978--3--319--92270--6_13

[18]

Benedikt G Mark, Erwin Rauch, and Dominik T Matt. 2021. Worker assistance systems in manufacturing : A review of the state of the art and future directions. Journal of Manufacturing Systems 59, March (2021), 228--250. https: //doi.org/10.1016/j.jmsy.2021.02.017

[19]

Sebastian Müller, Matthias Baldauf, Andreas Michel, and Peter Fröhlich. 2021. Advanced Ubiquitous Monitoring Services for Workers in Automated Production Environments. In Proceedings of Automation Experience at the Workplace. CEUR-WS. http://ceur-ws.org/Vol-2905/paper10.pdf

[20]

Andrew. Y.C. Nee, Soh . K. Ong, George. Chryssolouris, and Dimitris. Mourtzis. 2012. Augmented reality applications in design and manufacturing. CIRP Annals - Manufacturing Technology 61, 2 (2012), 657--679. https://doi.org/10.1016/j. cirp.2012.05.010

[21]

Antonio Padovano, Francesco Longo, Letizia Nicoletti, and Giovanni Mirabelli. 2018. A Digital Twin based Service Oriented Application for a 4.0 Knowledge Navigation in the Smart Factory. IFAC-Papers OnLine 51, 11 (2018), 631--636. https://doi.org/10.1016/j.ifacol.2018.08.389

[22]

Volker Paelke, Carsten Röcker, Nils Koch, Holger Flatt, and Sebastian Büttner. 2015. User interfaces for cyber-physical systems. at - Automatisierungstechnik 63, 10 (Oct. 2015), 833--843. https://doi.org/10.1515/auto-2015-0016

[23]

Emese Papp, Christian Wölfel, and Jens Krzywinski. 2020. Acceptance and User Experience of Wearable Assistive Devices for Industrial Purposes. In Proceedings of the Design Society: DESIGN Conference, Vol. 1. Cambridge University Press, 1515--1520. https://doi.org/10.1017/dsd.2020.319

[24]

Ana. C. Pereira and Fernando. Romero. 2017. A review of the meanings and the implications of the Industry 4.0 concept. Procedia Manufacturing 13 (2017), 1206--1214. https://doi.org/10.1016/j.promfg.2017.09.032

[25]

Sandra Robla-Gomez, Victor M. Becerra, Josè Ramòn Llata, Esther Gonzalez-Sarabia, Carlos Torre-Ferrero, and Juan Perez-Oria. 2017. Working Together: A Review on Safe Human-Robot Collaboration in Industrial Environments. IEEE Access 5 (2017), 26754--26773. https://doi.org/10.1109/ACCESS.2017.2773127

[26]

Arne Seeliger, Gerrit Merz, Christian Holz, and Stefan Feuerriegel. 2021. Exploring the Effect of Visual Cues on Eye Gaze During AR-Guided Picking and Assembly Tasks. In 2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). https://doi.org/10.1109/ISMAR-Adjunct54149.2021.00041

[27]

Arne Seeliger, Torbjörn Netland, and Stefan Feuerriegel. 2022. Augmented Reality for Machine Setups: Task Performance and Usability Evaluation in a Field Test. Procedia CIRP 107C (2022), 573--578.

[28]

Jane Siegel and Malcolm Bauer. 1997. Field usability evaluation of a wearable system. In International Symposium on Wearable Computers, Digest of Papers. 18--22. https://doi.org/10.1109/iswc.1997.629914

[29]

David Sousa Sousa Nunes, Pei Zhang, and Jorge Sa Silva. 2015. A Survey on human-in-The-loop applications towards an internet of all. IEEE Communications Surveys and Tutorials 17, 2 (2015), 944--965. https://doi.org/10.1109/COMST. 2015.2398816

[30]

Anna Syberfeldt, Oscar Danielsson, Magnus Holm, and Lihui Wang. 2015. Visual Assembling Guidance Using Augmented Reality. Procedia Manufacturing 1 (2015), 98--109. https://doi.org/10.1016/j.promfg.2015.09.068

[31]

Philipp Url, Wolfgang Vorraber, and Johannes Gasser. 2019. Practical insights on augmented reality support for shop-floor tasks. Procedia Manufacturing 39, 2019 (2019), 4--12. https://doi.org/10.1016/j.promfg.2020.01.222

[32]

Niels van Berkel, Jorge Goncalves, Simo Hosio, and Vassilis Kostakos. 2017. Gamification of Mobile Experience Sampling Improves Data Quality and Quantity. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 1--21. https://doi.org/10.1145/3130972

Digital Library

[33]

Susanne Vernim and Gunther Reinhart. 2016. Usage Frequency and User-Friendliness of Mobile Devices in Assembly. Procedia CIRP 57 (2016), 510--515. https://doi.org/10.1016/j.procir.2016.11.088

[34]

W Wanbin and P W Tse. 2006. Remote machine monitoring through mobile phone, smartphone or pda. In Engineering Asset Management. Springer, London, UK, 309--315.

[35]

Wenchao Wu, Yixian Zheng, Kaiyuan Chen, Xiangyu Wang, and Nan Cao. 2018. A Visual Analytics Approach for Equipment Condition Monitoring in Smart Factories of Process Industry. IEEE Pacific Visualization Symposium 2018-April (2018), 140--149. https://doi.org/10.1109/PacificVis.2018.00026

[36]

Panpan Xu, Honghui Mei, Liu Ren, and Wei Chen. 2017. ViDX: Visual Diagnostics of Assembly Line Performance in Smart Factories. IEEE Transactions on Visualization and Computer Graphics 23, 1 (2017), 291--300. https://doi.org/10. 1109/TVCG.2016.2598664

Digital Library

[37]

Steffen Zenker and Sebastian Hobert. 2020. Design and implementation of a collaborative smartwatch application supporting employees in industrial workflows. In 27th European Conference on Information Systems - Information Systems for a Sharing Society, ECIS 2019. Stockholm & Uppsala, Sweden, 0--16.

[38]

Xianjun Sam Zheng, Patrik Matos da Silva, Cedric Foucault, Siddharth Dasari, Meng Yuan, and Stuart Goose. 2015. Wearable Solution for Industrial Maintenance. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 311--314.

Digital Library

[39]

Fangfang Zhou, Xiaoru Lin, Chang Liu, Ying Zhao, Panpan Xu, Liu Ren, Tingmin Xue, and Lei Ren. 2019. A survey of visualization for smart manufacturing. Journal of Visualization 22, 2 (2019), 419--435. https://doi.org/10.1007/s12650- 018-0530--2

[40]

Fangfang Zhou, Xiaoru Lin, Xiaobo Luo, Ying Zhao, Yi Chen, Ning Chen, and Weihua Gui. 2018. Visually enhanced situation awareness for complex manufacturing facility monitoring in smart factories. Journal of Visual Languages and Computing 44 (2018), 58--69. https://doi.org/10.1016/j.jvlc.2017.11.004

Cited By

Ma ZLi JBai G(2024)ReLU Hull ApproximationProceedings of the ACM on Programming Languages10.1145/36329178:POPL(2260-2287)Online publication date: 5-Jan-2024
https://dl.acm.org/doi/10.1145/3632917
Boudardara FBoussif AMeyer PGhazel M(2024)A Review of Abstraction Methods Toward Verifying Neural NetworksACM Transactions on Embedded Computing Systems10.1145/361750823:4(1-19)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3617508
Naseer MHasan OShafique M(2023)QuanDA: GPU Accelerated Quantitative Deep Neural Network AnalysisACM Transactions on Design Automation of Electronic Systems10.1145/361167128:6(1-21)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3611671
Show More Cited By

Index Terms

Ubiquitous Machinery Monitoring - A Field Study on Manufacturing Workers' User Experience of Mobile and Wearable Monitoring Apps
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. Field studies
  2. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing

Recommendations

Human Interventions in the Smart Factory – A Case Study on Co-Designing Mobile and Wearable Monitoring Systems with Manufacturing Staff
CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

So-called smart factories with networked physical machinery and highly automated manufacturing processes offer huge potential for efficiency and productivity increases. While respective user-centered research has been investigating assistance solutions ...
Burnout Among Manufacturing Workers in China: The Effects of Organizational Socialization and Leadership Behavior

The relationship between organizational socialization Training, Understanding, Coworker Support, and Future Prospects and leadership behavior Monitor, Producer, Consideration for Others, and Trust in Others and burnout Emotional Exhaustion and Personal ...
Remote Workers’ Perceptions on Employee Monitoring
HCI International 2022 - Late Breaking Papers. Design, User Experience and Interaction
Abstract
With the prevalence of working from home, more and more organizations are adopting monitoring methods to keep track of their employees’ work activities electronically. Understanding how employees respond to various monitoring methods and what ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 6, Issue MHCI

MHCI

September 2022

852 pages

EISSN:2573-0142

DOI:10.1145/3564624

Editor:
Jeff Nichols
Apple Inc., United States

Issue’s Table of Contents

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2022

Published in PACMHCI Volume 6, Issue MHCI

Check for updates

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
445
Total Downloads

Downloads (Last 12 months)213
Downloads (Last 6 weeks)39

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ma ZLi JBai G(2024)ReLU Hull ApproximationProceedings of the ACM on Programming Languages10.1145/36329178:POPL(2260-2287)Online publication date: 5-Jan-2024
https://dl.acm.org/doi/10.1145/3632917
Boudardara FBoussif AMeyer PGhazel M(2024)A Review of Abstraction Methods Toward Verifying Neural NetworksACM Transactions on Embedded Computing Systems10.1145/361750823:4(1-19)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3617508
Naseer MHasan OShafique M(2023)QuanDA: GPU Accelerated Quantitative Deep Neural Network AnalysisACM Transactions on Design Automation of Electronic Systems10.1145/361167128:6(1-21)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3611671
Landers MDoryab A(2023)Deep Reinforcement Learning Verification: A SurveyACM Computing Surveys10.1145/359644455:14s(1-31)Online publication date: 6-May-2023
https://dl.acm.org/doi/10.1145/3596444
Yin BChen LLiu JWang J(2022)Efficient Complete Verification of Neural Networks via Layerwised Splitting and RefinementIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.319753441:11(3898-3909)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1109/TCAD.2022.3197534

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents