Abstract

We evaluate AR for Plant maintenance by measuring how a pro- totype guidance system, tested under representative conditions, impacts performance. We are motivated to determine the cost- benefit of interactive guidance for hazardous, repetitive tasks and we observe an improvement of 21% efficiency, 50% accuracy and 19% reduced task load. AR has already been shown to deliver im- provements in task performance, however, there is limited research exploring the integration of AR into complete task routines which presents a barrier to adoption. We apply mixed reality guidance via two within-group experiments. We measure efficiency and ac- curacy over a complete routine conducted under simulated condi- tions. Results compare AR versus Static and AR versus Adaptive. We conclude AR is best suited to demanding spatial translation and completion under pressure. We suggest AR offers potential in similar routines and propose further work to integrate in a live setting.
Original languageEnglish
Publication statusAccepted/In press - 18 May 2021
EventACM International Conference on Mobile Human-Computer Interaction - Toulouse, France
Duration: 27 Sep 20211 Oct 2021
https://mobilehci.acm.org/2021/

Conference

ConferenceACM International Conference on Mobile Human-Computer Interaction
Abbreviated titleMobileHCI 2021
CountryFrance
CityToulouse
Period27/09/211/10/21
Internet address

Fingerprint

Dive into the research topics of 'Evaluating Prototype Augmented and Adaptive guidance system to support Industrial Plant Maintenance'. Together they form a unique fingerprint.

Cite this