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dc.contributor.authorGuest, Will
dc.contributor.authorWild, Fridolin
dc.contributor.authorVovk, Alla
dc.contributor.authorFominykh, Mikhail
dc.contributor.authorLimbu, Bibeg
dc.contributor.authorKlemke, Roland
dc.contributor.authorSharma, Puneet
dc.contributor.authorKarjalainen, Jaakko
dc.contributor.authorSmith, Carl
dc.contributor.authorRasool, Jazz
dc.contributor.authorAswat, Soyeb
dc.contributor.authorHelin, Kaj
dc.contributor.authorDi Mitri, Daniele
dc.contributor.authorSchneider, Jan
dc.date.accessioned2018-03-13T13:15:09Z
dc.date.available2018-03-13T13:15:09Z
dc.date.issued2017-09-05
dc.description.abstractThe 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.en_US
dc.descriptionOA accepted manuscript version. Copyright policy: <a href=http://www.springer.com/gp/computer-science/lncs/editor-guidelines-for-springer-proceedings>http://www.springer.com/gp/computer-science/lncs/editor-guidelines-for-springer-proceedings</a> Link to publishers version: <a href=https://link.springer.com/chapter/10.1007/978-3-319-66610-5_34>https://link.springer.com/chapter/10.1007/978-3-319-66610-5_34</a>en_US
dc.identifier.citationGuest W, Wild F, Vovk A, Fominykh, Limbu B, Klemke R, Sharma P, Karjalainen J, Smith C, Rasool J, Aswat S, Helin K, Di Mitri D, Schneider J. Affordances for capturing and re-enacting expert performance with wearables. Lecture Notes in Computer Science. 2017;10474 LNCS:403-409en_US
dc.identifier.cristinIDFRIDAID 1542788
dc.identifier.doi10.1007/978-3-319-66610-5_34
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttps://hdl.handle.net/10037/12315
dc.language.isoengen_US
dc.publisherSpringer International Publishingen_US
dc.relation.ispartofseriesLecture Notes in Computer Science; 10474en_US
dc.relation.journalLecture Notes in Computer Science
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Technology: 500en_US
dc.titleAffordances for capturing and re-enacting expert performance with wearablesen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelno
dc.typePeer revieweden_US


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