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

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

doi:10.1145/3447526.3472042

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

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

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

1 Citation (Scopus)

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
Title of host publication	MobileHCI '21
Subtitle of host publication	Proceedings of the 23rd International Conference on Mobile Human-Computer Interaction
Publisher	Association for Computing Machinery (ACM)
Number of pages	10
ISBN (Print)	9781450383288
DOIs	https://doi.org/10.1145/3447526.3472042
Publication status	Published - 27 Sept 2021
Event	ACM International Conference on Mobile Human-Computer Interaction - Toulouse, France Duration: 27 Sept 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/

Research Groups and Themes

Bristol Interaction Group

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10.1145/3447526.3472042

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Augmented Reality guidance for fusion plant maintenance
Bale, T. A. K. (Author), Cater, K. (Supervisor), Scott, T. (Supervisor) & Calway, A. (Supervisor), 3 Oct 2023
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)

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Bale, T. A. K., Calway, A., Cater, K. F., Bevan, C. R., Scott , T. B., Skilton, R., & Scott, T. B. (2021). Evaluating Prototype Augmented and Adaptive guidance system to support Industrial Plant Maintenance. In MobileHCI '21: Proceedings of the 23rd International Conference on Mobile Human-Computer Interaction Article 40 Association for Computing Machinery (ACM). https://doi.org/10.1145/3447526.3472042

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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 Andrew Calway and Cater, {Kirsten F} and Bevan, {Chris R} and Scott, {Thomas Bligh} and Robert Skilton and Scott, {Thomas Bligh}",

year = "2021",

month = sep,

day = "27",

doi = "10.1145/3447526.3472042",

language = "English",

isbn = "9781450383288",

booktitle = "MobileHCI '21",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

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/",

}

Bale, TAK, Calway, A , Cater, KF , Bevan, CR , Scott , TB, Skilton, R & Scott, TB 2021, Evaluating Prototype Augmented and Adaptive guidance system to support Industrial Plant Maintenance. in MobileHCI '21: Proceedings of the 23rd International Conference on Mobile Human-Computer Interaction., 40, Association for Computing Machinery (ACM), ACM International Conference on Mobile Human-Computer Interaction, Toulouse, France, 27/09/21. https://doi.org/10.1145/3447526.3472042

Evaluating Prototype Augmented and Adaptive guidance system to support Industrial Plant Maintenance. / Bale, Thomas A K; Calway, Andrew ; Cater, Kirsten F et al.
MobileHCI '21: Proceedings of the 23rd International Conference on Mobile Human-Computer Interaction. Association for Computing Machinery (ACM), 2021. 40.

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

TY - GEN

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

AU - Bale, Thomas A K

AU - Calway, Andrew

AU - Cater, Kirsten F

AU - Bevan, Chris R

AU - Scott , Thomas Bligh

AU - Skilton, Robert

AU - Scott, Thomas Bligh

PY - 2021/9/27

Y1 - 2021/9/27

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.

U2 - 10.1145/3447526.3472042

DO - 10.1145/3447526.3472042

M3 - Conference Contribution (Conference Proceeding)

SN - 9781450383288

BT - MobileHCI '21

PB - Association for Computing Machinery (ACM)

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

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Student theses

Augmented Reality guidance for fusion plant maintenance

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