CAARL is a freely accessible logistics-dataset for human activity recognition, which contains human movement and context information from two subjects. The context information includes the positions of objects such as two picking carts, a packaging table, different racks, a base and three entrances. In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios were recorded using an optical marker based motion capture system. Each subject and object is equipped with several markers. 140 minutes of human movements have been labelled and categorised into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. The labelled human movements are synchronised with the context information. They have exactly the same sampling rate (same start and end). The oMoCap data is in csv format. Further formats (e.g. C3D) are available on request. CAARL is based on the set-up and scenarios of the LARa dataset, which contains only human movements. Information about LARa can be found in the dataset and the associated paper: Dataset: “Logistic Activity Recognition Challenge (LARa) – A Motion Capture and Inertial Measurement Dataset”, Zenodo 2020, DOI: 10.5281/zenodo.3862782 Paper: “LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes”, Sensors 2020, DOI: 10.3390/s20154083 If you use the CAARL dataset for research, please cite the following paper: “Context-Aware Human Activity Recognition in Industrial Processes”, Sensors 2021, DOI: 10.3390/s22010134

{"references": ["Niemann, Friedrich et al. (2020). Journal article: \"LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes\", Sensors 2020, DOI: 10.3390/s20154083", "Niemann, Friedrich et al. (2020). Dataset: \"Logistic Activity Recognition Challenge (LARa) \u2013 A Motion Capture and Inertial Measurement Dataset\", Zenodo 2020, DOI: 10.5281/zenodo.3862782"]}

Acknowledgement: This research was funded by the German Research Foundation (grant numbers: Fi799/10-2, HO2403/14-2) and the Federal Ministry of Transport and Digital Infrastructure (grant number: 45KI02B021).