Zimmer_ATDE-15-ATDE210008 (1).pdf (503.79 kB)
Download fileMental workload of local vs remote operator in human-machine interaction case study
conference contribution
posted on 2021-09-17, 10:13 authored by Melanie Zimmer, Ali Al-Yacoub, Pedro FerreiraPedro Ferreira, Ella-Mae HubbardElla-Mae Hubbard, Niels LohseNiels LohseSince late 2019, a novel Coronavirus disease 2019 (COVID-19) has spread globally. As a result, businesses were forced to send their workforce into remote working, wherever possible. While research in this area has seen an increase in studying and developing technologies that allow and support such remote working style, not every sector is currently prepared for such a transition. Especially the manufacturing sector has faced challenges in this regard. In this paper, the mental workload of two groups of participants is studied during a human-robot interaction task. Participants were asked to bring a robotised cell used in a dispensing task to full production by tuning system parameters. After the experiment, a self-assessment of the participants’ perceived mental workload using the NASA Task Load Index (NASA-TLX) was used. The results show that remote participants tend to have lower perceived workload compared to the local participants.
Funding
EPSRC Centre for Doctoral Training in Embedded Intelligence
Engineering and Physical Sciences Research Council
Find out more...Digital Toolkit for optimisation of operators and technology in manufacturing partnerships (DigiTOP)
Engineering and Physical Sciences Research Council
Find out more...History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
Advances in Manufacturing Technology XXXIV. Proceedings of the 18th International Conference on Manufacturing Research, incorporating the 35th National Conference on Manufacturing Research, 7-10 September 2021, University of Derby, Derby, UKPages
33-38Source
18th International Conference in Manufacturing Research (ICMR 2021)Publisher
IOS PressVersion
- VoR (Version of Record)
Rights holder
© The AuthorsPublisher statement
This is an Open Access Article. It is published by IOS Press under the Creative Commons Attribution-Non Commercial 4.0 International Licence (CC BY-NC). Full details of this licence are available at: https://creativecommons.org/licenses/by-nc/4.0/Acceptance date
2021-05-18Publication date
2021-09-31Copyright date
2021ISBN
9781643681986; 9781643681993Publisher version
Book series
Advances in Transdisciplinary Engineering. Volume 15: Advances in Manufacturing Technology XXXIVLanguage
- en