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

Thomas A K Bale; Chris R Bevan; Andrew Calway; Kirsten F Cater; Thomas Bligh Scott; Robert Skilton

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

Thomas A K Bale, Chris R Bevan, Andrew Calway, Kirsten F Cater, Thomas Bligh Scott , Robert Skilton

Research output: Contribution to conference › Conference Paper › peer-review

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 language	English
Publication status	Accepted/In press - 18 May 2021
Event	ACM International Conference on Mobile Human-Computer Interaction - Toulouse, France Duration: 27 Sep 2021 → 1 Oct 2021 https://mobilehci.acm.org/2021/

Conference

Conference	ACM International Conference on Mobile Human-Computer Interaction
Abbreviated title	MobileHCI 2021
Country/Territory	France
City	Toulouse
Period	27/09/21 → 1/10/21
Internet address	https://mobilehci.acm.org/2021/

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title = "Evaluating Prototype Augmented and Adaptive guidance system to support Industrial Plant Maintenance",

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.",

author = "Bale, {Thomas A K} and Bevan, {Chris R} and Andrew Calway and Cater, {Kirsten F} and Scott, {Thomas Bligh} and Robert Skilton",

year = "2021",

month = may,

day = "18",

language = "English",

note = "ACM International Conference on Mobile Human-Computer Interaction, MobileHCI 2021 ; Conference date: 27-09-2021 Through 01-10-2021",

url = "https://mobilehci.acm.org/2021/",

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TY - CONF

T1 - Evaluating Prototype Augmented and Adaptive guidance system to support Industrial Plant Maintenance

AU - Bale, Thomas A K

AU - Bevan, Chris R

AU - Calway, Andrew

AU - Cater, Kirsten F

AU - Scott , Thomas Bligh

AU - Skilton, Robert

PY - 2021/5/18

Y1 - 2021/5/18

N2 - 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.

AB - 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.

M3 - Conference Paper

T2 - ACM International Conference on Mobile Human-Computer Interaction

Y2 - 27 September 2021 through 1 October 2021

ER -

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

Abstract

Conference

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