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Handling Work Complexity with AR/Deep Learning

: Dhiman, Hitesh; Büttner, Sebastian; Röcker, Carsten; Reisch, Raphael


Association for Computing Machinery -ACM-:
OZCHI '19: Proceedings of the 31st Australian Conference on Human-Computer-Interaction : Fremantle, WA, Australia December, 2019
New York: ACM, 2019
ISBN: 978-1-4503-7696-9
Australian Conference on Computer-Human Interaction (OZCHI) <31, 2019, Fremantle>
Fraunhofer IOSB ()
Augmented reality; Deep Learning

Complexity is a fundamental part of product design and manufacturing today, owing to increased demands for customization and advances in digital design techniques. Assembling and repairing such an enormous variety of components means that workers are cognitively challenged, take longer to search for the relevant information and are prone to making mistakes. Although in recent years deep learning approaches to object recognition have seen rapid advances, the combined potential of deep learning and augmented reality in the industrial domain remains relatively under explored. In this paper we introduce AR-ProMO, a combined hardware/software solution that provides a generalizable assistance system for identifying mistakes during product assembly and repair.