WEKIT.One: A Sensor-Based Augmented Reality System for Experience Capture and Re-enactment

Bibeg Limbu*, Alla Vovk, Halszka Jarodzka, Roland Klemke, Fridolin Wild, Marcus Specht

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

Body-worn sensors can be used to capture, analyze, and replay human performance for training purposes. The key challenge to any such approach is to establish validity that the captured expert experience is actually suitable for training. In this paper, to evaluate this, we apply a questionnaire-based expert assessment and a complementary trainee knowledge assessment to study the approach adopted and the models generated with the WEKIT solution, a hardware and software application that complements Augmented Reality glasses with wearable sensor-actuator experience. This solution was developed using the ID4AR framework which as also developed within the WEKIT project. ID4AR framework is a domain agnostic framework which can be used to design augmented reality and sensor based applications for training. The study presented triangulates validity across three independent test-beds in the professional domains of aircraft maintenance, medical imaging, and astronaut training, with 61 experts completing the expert survey and 337 students completing the trainee knowledge test. Results show that the captured expert models were positively received in all three domains and the identified level of acceptance suggests that the solution is capable of capturing models for training purposes at large.
Original languageEnglish
Title of host publicationTransforming Learning with Meaningful Technologies
Subtitle of host publication14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Delft, The Netherlands, September 16–19, 2019, Proceedings
EditorsMaren Scheffel, Julien Broisin, Viktoria Pammer-Schindler, Andri Ioannou, Jan Schneider
Place of PublicationCham
PublisherSpringer
Chapter12
Pages158-171
Number of pages14
ISBN (Electronic)9783030297367
ISBN (Print)9783030297350
DOIs
Publication statusPublished - 9 Sep 2019
Event14th European Conference on Technology Enhanced Learning: Transforming Learning With Meaningful Technologies - The Leiden-Delft-Erasmus Center for Education and Learning , Delft, Netherlands
Duration: 16 Sep 201919 Sep 2019
Conference number: 2019
http://www.ec-tel.eu
http://www.ec-tel.eu/index.php?id=918

Publication series

SeriesLecture Notes in Computer Science
Volume11722
ISSN0302-9743

Conference

Conference14th European Conference on Technology Enhanced Learning
Abbreviated titleEC-TEL 2019
CountryNetherlands
CityDelft
Period16/09/1919/09/19
Internet address

Fingerprint

Augmented reality
Sensors
Medical imaging
Application programs
Computer hardware
Actuators
Aircraft
Students
Glass

Cite this

Limbu, B., Vovk, A., Jarodzka, H., Klemke, R., Wild, F., & Specht, M. (2019). WEKIT.One: A Sensor-Based Augmented Reality System for Experience Capture and Re-enactment. In M. Scheffel, J. Broisin, V. Pammer-Schindler, A. Ioannou, & J. Schneider (Eds.), Transforming Learning with Meaningful Technologies: 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Delft, The Netherlands, September 16–19, 2019, Proceedings (pp. 158-171). Cham: Springer. Lecture Notes in Computer Science, Vol.. 11722 https://doi.org/10.1007/978-3-030-29736-7_12
Limbu, Bibeg ; Vovk, Alla ; Jarodzka, Halszka ; Klemke, Roland ; Wild, Fridolin ; Specht, Marcus. / WEKIT.One: A Sensor-Based Augmented Reality System for Experience Capture and Re-enactment. Transforming Learning with Meaningful Technologies: 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Delft, The Netherlands, September 16–19, 2019, Proceedings. editor / Maren Scheffel ; Julien Broisin ; Viktoria Pammer-Schindler ; Andri Ioannou ; Jan Schneider. Cham : Springer, 2019. pp. 158-171 (Lecture Notes in Computer Science, Vol. 11722).
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Limbu, B, Vovk, A, Jarodzka, H, Klemke, R, Wild, F & Specht, M 2019, WEKIT.One: A Sensor-Based Augmented Reality System for Experience Capture and Re-enactment. in M Scheffel, J Broisin, V Pammer-Schindler, A Ioannou & J Schneider (eds), Transforming Learning with Meaningful Technologies: 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Delft, The Netherlands, September 16–19, 2019, Proceedings. Springer, Cham, Lecture Notes in Computer Science, vol. 11722, pp. 158-171, 14th European Conference on Technology Enhanced Learning, Delft, Netherlands, 16/09/19. https://doi.org/10.1007/978-3-030-29736-7_12

WEKIT.One: A Sensor-Based Augmented Reality System for Experience Capture and Re-enactment. / Limbu, Bibeg; Vovk, Alla; Jarodzka, Halszka; Klemke, Roland; Wild, Fridolin; Specht, Marcus.

Transforming Learning with Meaningful Technologies: 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Delft, The Netherlands, September 16–19, 2019, Proceedings. ed. / Maren Scheffel; Julien Broisin; Viktoria Pammer-Schindler; Andri Ioannou; Jan Schneider. Cham : Springer, 2019. p. 158-171 (Lecture Notes in Computer Science, Vol. 11722).

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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AU - Wild, Fridolin

AU - Specht, Marcus

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AB - Body-worn sensors can be used to capture, analyze, and replay human performance for training purposes. The key challenge to any such approach is to establish validity that the captured expert experience is actually suitable for training. In this paper, to evaluate this, we apply a questionnaire-based expert assessment and a complementary trainee knowledge assessment to study the approach adopted and the models generated with the WEKIT solution, a hardware and software application that complements Augmented Reality glasses with wearable sensor-actuator experience. This solution was developed using the ID4AR framework which as also developed within the WEKIT project. ID4AR framework is a domain agnostic framework which can be used to design augmented reality and sensor based applications for training. The study presented triangulates validity across three independent test-beds in the professional domains of aircraft maintenance, medical imaging, and astronaut training, with 61 experts completing the expert survey and 337 students completing the trainee knowledge test. Results show that the captured expert models were positively received in all three domains and the identified level of acceptance suggests that the solution is capable of capturing models for training purposes at large.

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Limbu B, Vovk A, Jarodzka H, Klemke R, Wild F, Specht M. WEKIT.One: A Sensor-Based Augmented Reality System for Experience Capture and Re-enactment. In Scheffel M, Broisin J, Pammer-Schindler V, Ioannou A, Schneider J, editors, Transforming Learning with Meaningful Technologies: 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Delft, The Netherlands, September 16–19, 2019, Proceedings. Cham: Springer. 2019. p. 158-171. (Lecture Notes in Computer Science, Vol. 11722). https://doi.org/10.1007/978-3-030-29736-7_12