You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

Classifying Excavator Collisions Based on Users’ Visual Perception in the Mixed Reality Environment

Fulltext:


Note:

This is the accepted version of the conference paper presented at HUCAPP 2021 (http://www.hucapp.visigrapp.org/?y=2021). The final published version is available at Scitepress via https://doi.org/10.5220/0010386702550262. Personal use of this material is permitted. Permission from Scitepress must be obtained for all other uses.

Research group:


Publication Type:

Conference/Workshop Paper

Venue:

5th International Conference on Human Computer Interaction Theory and Applications

Publisher:

Scitepress

DOI:

https://doi.org/10.5220/0010386702550262


Abstract

Visual perception plays an important role for recognizing possible hazards. In the context of heavy machinery, relevant visual information can be obtained from the machine's surrounding and from the human-machine interface that exists inside the cabin. In this paper, we propose a method that classifies the occurring collisions by combining the data collected by the eye tracker and the automatic logging mechanism in the mixed reality simulation. Thirteen participants were asked to complete a test scenario in the mixed reality simulation, while wearing an eye tracker. The results demonstrate that we could classify the occurring collisions based on two visual perception conditions: (1) whether the colliding objects were visible from the participants' field of view and (2) whether the participants have seen the information presented on the human-machine interface before the collisions occurred. This approach enabled us to interpret the occurring collisions differently, compared to the traditional approach that uses the total number of collisions as the representation of participants' performance.

Bibtex

@inproceedings{Forsman6108,
author = {Viking Forsman and Markus Wallmyr and Taufik Akbar Sitompul and Rikard Lindell},
title = {Classifying Excavator Collisions Based on Users’ Visual Perception in the Mixed Reality Environment},
isbn = {978-989-758-488-6},
note = {This is the accepted version of the conference paper presented at HUCAPP 2021 (http://www.hucapp.visigrapp.org/?y=2021). The final published version is available at Scitepress via https://doi.org/10.5220/0010386702550262. Personal use of this material is permitted. Permission from Scitepress must be obtained for all other uses.},
pages = {255--262},
month = {February},
year = {2021},
booktitle = {5th International Conference on Human Computer Interaction Theory and Applications},
publisher = {Scitepress},
url = {http://www.es.mdh.se/publications/6108-}
}