Work

Assessing AR Visualizations during Non-Complex Assembly: Reporting Preliminary Results from a Crowdsourcing Rework Study

Year: 2024

Type: conference-paper

Abstract: This study assesses the viability of using a simple assembly task as part of a crowdsourcing experiment to evaluate different visualizations in augmented reality (AR). We recruited 36 participants through a crowdsourcing platform to assess how visualization types (i.e., none, contour, and abstract) impact the performance of a gear-box assembly rework task. Our preliminary results suggest that the learning effect associated with manual assembly of a non-complex gearbox object plays a more significant role in improving performance than the type of visualization used. This pilot study also highlights requirements for crowdsourced task difficulty in order to offer potentially viable insights.

Source: IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)

DOI: 10.1109/ismar-adjunct64951.2024.00025

Authors: Alexander Albers, Magdalena Igras-Cybulska, Sławomir K. Tadeja, Thomas Bohné

Institutions: ETH Zurich, University of Cambridge

Language: English

Topic: Augmented Reality Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: UK Research and Innovation, Engineering and Physical Sciences Research Council

Awards: G109370583, Made Smarter Innovation - Research Centre for Connected Factories