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In this paper we describe and evaluate a mixed reality system that aims toaugment users in task guidance applications by combining automated andunsupervised information collection with minimally invasive video guides. Theresult is a self-contained system that we call GlaciAR (Glass-enabledContextual Interactions for Augmented Reality), that operates by extractingcontextual interactions from observing users performing actions. GlaciAR isable to i) automatically determine moments of relevance based on a head motionattention model, ii) automatically produce video guidance information, iii)trigger these video guides based on an object detection method, iv) learnwithout supervision from observing multiple users and v) operate fully on-boarda current eyewear computer (Google Glass). We describe the components ofGlaciAR together with evaluations on how users are able to use the system toachieve three tasks. We see this work as a first step toward the development ofsystems that aim to scale up the notoriously difficult authoring problem inguidance systems and where people's natural abilities are enhanced viaminimally invasive visual guidance.

Automated capture and delivery of assistive task guidance with an eyewear computer: The GlaciAR system