Applications of human activity recognition in industrial processes -- Synergy of human and technology
This work addresses the need for efficient human-technology collaboration in industrial settings, though it appears incremental as it builds on existing recognition methods with specific enhancements.
The paper tackles the problem of human activity recognition for non-verbal communication in industrial intralogistics, showing that using semantic attributes and context information improves classifier performance, and proposing a cyber-physical twin concept to reduce training dataset creation effort and time.
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.