Context-Aware Support of Dexterity Skills in Cross-Reality Environments

Within our work, we apply context-awareness to determine how AR/VR technology should adapt instructions based on the context to suit user needs. We focus on situations where the user must carry out a complex manual activity that requires additional information to be present during the activity to ac...

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Vydáno v:IEEE International Symposium on Mixed and Augmented Reality Workshops (Online) s. 954 - 957
Hlavní autoři: Ceyssens, Jeroen, Di Fiore, Fabian, Luyten, Kris
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2022
Témata:
Adaptation models
Cognitive load
Design methodology
Human-centered computing-Interaction design-Interaction design process and methods-Activity centered design Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed / augmented reality
Real-time systems
Training
Usability
X reality
ISSN:2771-1110
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Shrnutí:Within our work, we apply context-awareness to determine how AR/VR technology should adapt instructions based on the context to suit user needs. We focus on situations where the user must carry out a complex manual activity that requires additional information to be present during the activity to achieve the desired result. To this end, the emphasis is on activities that require fine-motor skills and in-depth expertise and training, for which XR is a powerful tool to support and guide users performing these tasks. The contexts we detect include user intentions, environmental conditions, and activity progressions. Our work builds on these contexts with the main focus on determining how XR should adapt for the end-user from a usability perspective. The feedback we request from ISMAR consists of input in detection, usability, and simulation categories, together with how to balance these categories to create real-time and user-friendly systems. The next steps of our work will consider how to content should adjust based on the cognitive load, activity space, and environmental conditions.
ISSN:2771-1110
DOI:10.1109/ISMAR-Adjunct57072.2022.00214