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.
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Acknowledgements
This study is partially funded by the WEKIT project, the Safepat project, and the TCBL project. The WEKIT project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 687669. The Safepat project is co-funded by the European Union under the Interreg EMR Programme. The TCBL project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 646133.
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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: Scheffel, M., Broisin, J., Pammer-Schindler, V., Ioannou, A., Schneider, J. (eds) Transforming Learning with Meaningful Technologies. EC-TEL 2019. Lecture Notes in Computer Science(), vol 11722. Springer, Cham. https://doi.org/10.1007/978-3-030-29736-7_12
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DOI: https://doi.org/10.1007/978-3-030-29736-7_12
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