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Context-Aware Human Activity Recognition in Industrial Processes.

Friedrich Niemann¹, Stefan Lüdtke², Christian Bartelt²

¹Chair of Materials Handling and Warehousing, TU Dortmund University, Joseph-von-Fraunhofer-Str. 2-4, 44227 Dortmund, Germany.

Sensors (Basel, Switzerland)

|January 11, 2022

Summary

This summary is machine-generated.

Context-aware human activity recognition in logistics improves process optimization. Integrating movement and context data significantly enhances accuracy for better production and warehouse management.

Keywords:

context awareness context model human activity recognition industrial processes logistics motion capture warehousing

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Area of Science:

Industrial Engineering
Human-Computer Interaction
Robotics

Background:

Automatic, sensor-based human activity recognition is crucial for optimizing production and logistics economics and ergonomics.
A key challenge is context-dependence, where similar movements signify different activities based on factors like object handled or location.

Purpose of the Study:

To develop an activity recognition model that explicitly incorporates context information.
To introduce a publicly available, semantically annotated motion capturing dataset for order picking and packaging activities with explicit context recording.

Main Methods:

Development of a novel activity recognition model integrating both movement data and contextual information.
Creation and utilization of a motion capturing dataset annotated with semantic context.

Main Results:

Activity recognition performance significantly increased when context information was integrated into the model.
Analysis identified key context features most influential for accurate activity recognition.

Conclusions:

Explicitly using context information is vital for improving human activity recognition in industrial settings.
The findings aid in designing effective sensor setups for real-world warehouse time management and process optimization.