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lehre:ws18:fsm_18ws:group_g [01.02.2019 22:53]
Christine Schikora
lehre:ws18:fsm_18ws:group_g [26.03.2019 11:54] (aktuell)
Christine Schikora
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 members_ ​           : Marlena Wolfes, Christine Schikora, Sümeyye Reyyan Yildiran members_ ​           : Marlena Wolfes, Christine Schikora, Sümeyye Reyyan Yildiran
 keywords_ ​          : WIP, AR, user study, augmented reality keywords_ ​          : WIP, AR, user study, augmented reality
-photo_img ​          : {{ :​lehre:​ws18:​fsm_18ws:​group-g-ar-pic.jpg?&400|Photo by Daniel Frank on Unsplash}}+photo_img ​          : {{:​lehre:​ws18:​fsm_18ws:​tempsnip.png?700|}}
-shortdescription ​   : This research deals with the question why AR applications are not used, although AR represents an emerging field with promising technology ​and developments. With our research we want to offer an overview over the complex topic and huge amount ​of literatureas we aim to classify ​AR applications ​based on the used technology ​as well as evaluation methodsFollowing this approach we will investigatewhich evaluation methods ​are useful for which kind of AR applications ​and possibly provide ​methods and guidelines that could serve as guidelines for the development ​of AR applications ​and their studiesSo basically, ​we want to make AR great again.+shortdescription ​   : The emergence of Augmented Reality (AR) in the last decades was characterized by a shift from exclusively industrial ​and governmental ​to commercial accessibility. This includes ​an expansion and advancement of the underlying technologies ​and therefore an increasing complexity and loss of overview of available methods. For this reasonit is difficult for developers ​to find suitable technologies and methods for evaluating ​AR applications ​that offer high validity and reliability. To overcome this issue, a taxonomy could provide an overview over all available possibilities ​as well as illustrate the linkage between single elements within a larger hierarchyHoweverin the literature few classifications of AR technologies and evaluation methods ​can be found. In our paper we propose taxonomies ​of AR technologies ​and evaluation ​methods and show their applicability,​ generalizability and expandability by analyzing state-of-the-art ​AR publications ​and the used technologies and evaluation methodsBy applying different selection criteria ​we reduced the corpus of publications from 405 to 135 covering the years 2015 to 2017.We analyzed usage frequencies and drew comparisons to results from the literature as well as identified limitations,​ problems and trends in current ​AR research. The most preferred technologies in our analysis are represented by visual displays with head-mounted or handheld positionings,​ optical tracking sensors as well as touch and body motion as input types. Regarding evaluation-related results, user performance and system technology as well as task-based application tests were the most preferred evaluation areas and methods.