Affordances for Capturing and Re-enacting Expert Performance with Wearables

Guest, Will, Wild, Fridolin, Volk, Alla, Fominykh, Mikhail, Limbu, Bibeg, Klemke, Roland, Sharma, Puneet, Karjalainen, Jaakko, Smith, Carl, Rasool, Jazz, Aswat, Soyeb, Helin, Kaj, Di Mitri, Daniele and Schneider, Jan (2017) Affordances for Capturing and Re-enacting Expert Performance with Wearables. In: Data Driven Approaches in Digital Education: 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Tallinn, Estonia, September 12–15, 2017, Proceedings. Lecture Notes in Computer Science, 10474 . Springer. ISBN 978-3-319-66610-5

Abstract

The WEKIT.one prototype is a platform for immersive procedural training with wearable sensors and Augmented Reality. Focusing on capture and re-enactment of human expertise, this work looks at the unique affordances of suitable hard- and software technologies. The practical challenges of interpreting expertise, using suitable sensors for its capture and specifying the means to describe and display to the novice are of central significance here. We link affordances with hardware devices, discussing their alternatives, including Microsoft Hololens, Thalmic Labs MYO, Alex Posture sensor, MyndPlay EEG headband, and a heart rate sensor. Following the selection of sensors, we describe integration and communication requirements for the prototype. We close with thoughts on the wider possibilities for implementation and next steps.

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