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/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

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 - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Proceedings
EditorsMaren Scheffel, Julien Broisin, Viktoria Pammer-Schindler, Andri Ioannou, Jan Schneider
PublisherSpringer
Pages158-171
Number of pages14
ISBN (Print)9783030297350
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event14th European Conference on Technology Enhanced Learning, EC-TEL 2019 - Delft, Netherlands
Duration: 16 Sept 201919 Sept 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11722 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th European Conference on Technology Enhanced Learning, EC-TEL 2019
Country/TerritoryNetherlands
CityDelft
Period16/09/1919/09/19

Keywords

  • Augmented Reality
  • Expert model
  • Sensors
  • Training

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