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Open AccessArticle

LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes

1
Chair of Materials Handling and Warehousing, TU Dortmund University, Joseph-von-Fraunhofer-Str. 2-4, 44227 Dortmund, Germany
2
Pattern Recognition in Embedded Systems Groups, TU Dortmund University, Otto-Hahn-Str. 16, 44227 Dortmund, Germany
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4083; https://doi.org/10.3390/s20154083
Received: 30 May 2020 / Revised: 2 July 2020 / Accepted: 20 July 2020 / Published: 22 July 2020
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
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. View Full-Text
Keywords: human activity recognition; attribute-based representation; dataset; motion capturing; inertial measurement unit; logistics human activity recognition; attribute-based representation; dataset; motion capturing; inertial measurement unit; logistics
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MDPI and ACS Style

Niemann, F.; Reining, C.; Moya Rueda, F.; Nair, N.R.; Steffens, J.A.; Fink, G.A.; ten Hompel, M. LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes. Sensors 2020, 20, 4083. https://doi.org/10.3390/s20154083

AMA Style

Niemann F, Reining C, Moya Rueda F, Nair NR, Steffens JA, Fink GA, ten Hompel M. LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes. Sensors. 2020; 20(15):4083. https://doi.org/10.3390/s20154083

Chicago/Turabian Style

Niemann, Friedrich; Reining, Christopher; Moya Rueda, Fernando; Nair, Nilah R.; Steffens, Janine A.; Fink, Gernot A.; ten Hompel, Michael. 2020. "LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes" Sensors 20, no. 15: 4083. https://doi.org/10.3390/s20154083

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