Friedrich Niemann, and Christopher Reining, Fernando Moya Rueda and
Nilah Ravi Nair, Janine Anika Steffens , Gernot A. Fink and Michael ten Hompel
Sensors, 20(15), 2020.
{Optimizations} in logistics require recognition and analysis of human activities. {The} potential of sensor-based human activity recognition (HAR) in logistics is not yet well explored. {Despite} a significant increase in HAR datasets in the past twenty years, no available dataset depicts activities in logistics. {This} contribution presents the first freely accessible logistics-dataset. {In} the 'Innovationlab Hybrid Services in Logistics' at TU Dortmund University, two picking and one packing scenarios were recreated. {Fourteen} subjects were recorded individually when performing warehousing activities using Optical marker-based Motion Capture (OMoCap), inertial measurement units (IMUs), and an RGB camera. {A} total of 758 min of recordings were labeled by 12 annotators in 474 person-h. {All} the given data have been labeled and categorized into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. {The} dataset is deployed for solving HAR using deep networks.