Evaluating Prototype Augmented and Adaptive guidance system to support Industrial Plant Maintenance

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)


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
Title of host publicationMobileHCI '21
Subtitle of host publicationProceedings of the 23rd International Conference on Mobile Human-Computer Interaction
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Print)978-1-4503-8328-8
Publication statusPublished - 27 Sept 2021
EventACM International Conference on Mobile Human-Computer Interaction - Toulouse, France
Duration: 27 Sept 20211 Oct 2021


ConferenceACM International Conference on Mobile Human-Computer Interaction
Abbreviated titleMobileHCI 2021
Internet address

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