Industry 4.0 is a coordinated push for automation in Smart Factories and other Cyber-Physical Systems (CPS). The increasing complexity of frequently changing production environments challenges shop floor workers to perform well. The tasks they work on are getting less routine and ask for continuous knowledge and skills development. For example, the skills portfolio of workers likely requires improved higher-order thinking and decisionmaking skills. A wide range of research and development efforts already today sets focus on different areas of workplace learning, including performance appraisals, pedagogy and education, technology, and business economics. Bridging the skills gap, however, requires novel user-facing technologies -such as Augmented Reality (AR) and wearables -for human performance augmentation to improve efficiency and effectiveness of staff delivered through live guidance. AR branches out beyond mobile apps with 3Dobject superimposition for marketing purposes to rather complex use cases delivered by a rapidly growing innovation ecosystem of hard-and software providers collaborating closely with R&D organisations. This paper provides a first shared vision on how AR can tackle four different challenges related to handling complexity in a CPS environment: develop intelligent assistance systems for learning and performance assessment at the workplace, adapt job profiles accordingly, and last but not least to address also the issue of work-life balance. The paper concludes with an outline of a research roadmap.

No abstract

Participants (N D 78) studied a visualization of a route through a complex building and walked that route in the real building without further assistance. Erroneous turns on the route as well as indicators of uncertainty such as hesitations were assessed. Three types of route visualizations were compared: (1) an allocentric, mapbased visualization with the route indicated in floor maps, (2) an ordered sequence of pictures of decision points shown from the egocentric perspective, and (3) an animation showing a virtual walk of the route from the egocentric perspective. In addition to the experimental variation, gender differences, differences in visual-spatial abilities and differences in self-reported wayfinding strategies were considered as predictor variables. Wayfinding performance did not differ between allocentric (map) and egocentric (decision point pictures and animation) visualizations. However, wayfinding performance was better with animated than with static egocentric visualizations. Individual differences in the ability to encode visual-spatial information from the visualization played a critical role for route learning. Self-reported sense of direction related to egocentric wayfinding strategies also predicted wayfinding performance. Gender differences were attributable to differences in visual-spatial abilities and egocentric wayfinding strategies. Interactions between visualizations and individual differences were not found. It is concluded that animations of virtual walks are suitable to convey route information in complex buildings. Successful acquisition of route knowledge from maps is possible but might depend on the comprehensibility of the structure of the building.

No abstract

No abstract