  Human-technology collaboration relies on verbal and non-verbal communication.
Machines must be able to detect and understand the movements of humans to
facilitate non-verbal communication. In this article, we introduce ongoing
research on human activity recognition in intralogistics, and show how it can
be applied in industrial settings. We show how semantic attributes can be used
to describe human activities flexibly and how context informantion increases
the performance of classifiers to recognise them automatically. Beyond that, we
present a concept based on a cyber-physical twin that can reduce the effort and
time necessary to create a training dataset for human activity recognition. In
the future, it will be possible to train a classifier solely with realistic
simulation data, while maintaining or even increasing the classification
performance.
