LARa is the first freely accessible logistics-dataset for human activity recognition. In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios with 14 subjects were recorded using OMoCap, IMUs, and an RGB camera. 758 minutes of recordings were labeled by 12 annotators in 474 person-hours. The subsequent revision was carried out by 4 revisers in 143 person-hours. All the given data have been labeled and categorised into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. You can find the latest version of the annotation tool here: https://github.com/wilfer9008/Annotation_Tool_LARa If you use this dataset for research, please cite the following paper: “LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes”, Sensors 2020, DOI: 10.3390/s20154083

Acknowledgement: The work on this publication was supported by Deutsche Forschungsgemeinschaft (DFG) in the context of the project Fi799/10-2, HO2403/14-2 "Transfer Learning for Human Activity Recognition in Logistics".