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

Applications of human activity recognition in industrial processes -- Synergy of human and technology