Abstract. We present an initial investigation from a semi-experimental setting, in which an HMD-based AR-system has been used for real-time collaboration in a task-oriented scenario (design of a museum exhibition). Analysis points out the specific conditions of interacting in an AR environment and focuses on one particular practical problem for the participants in coordinating their interaction: how to establish joint attention towards the same object or referent. Analysis allows insights into how the pair of users begins to familarize with the environment, the limitations and opportunities of the setting and how they establish new routines for e.g. solving the ʻjoint attentionʼ-problem.
This paper presents a short evaluation of auditory representations for object interactions as support for cooperating users of an Augmented Reality(AR) system. Particularly head-mounted AR displays limit the field of view and thus cause users to miss relevant activities of their interaction partner, such as object interactions or deictic references that normally would be effective to establish joint attention. We start from an analysis of the differences between face-toface interaction and interaction via the AR system, using interaction linguistic conversation analysis. From that we derive a set of features that are relevant for interaction partners to co-ordinate their activities. We then present five different interactive sonifications which make object manipulations of interaction partners audible by sonification that convey information about the kind of activity.
Multimodal research in human interaction has to consider a variety of factors, ranging from local short-time phenomena to complex interaction patterns. As of today, no single discipline engaged in communication research offers the methods and tools to investigate the full complexity continuum in a time-efficient way. A synthesis of qualitative and quantitative analysis is required to merge insights about micro-sequential structures with big data patterns. Using the example of a co-present dyadic negotiation analysis to combine methods offered by Conversation Analysis and Data Mining, we show how such a partnership can benefit each discipline and lead to insights as well as new hypotheses evaluation opportunities.
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