Dataset Open Access

Logistic Activity Recognition Challenge (LARa Version 02) – A Motion Capture and Inertial Measurement Dataset

Niemann, Friedrich; Reining, Christopher; Moya Rueda, Fernando; Bas, Hülya; Altermann, Erik; Nair, Nilah Ravi; Steffens, Janine Anika; Fink, Gernot A.; ten Hompel, Michael


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    "description": "<p><strong>LARa</strong><strong> Version 02</strong>&nbsp;is a freely accessible logistics-dataset for human activity recognition. In the &rsquo;Innovationlab Hybrid Services in Logistics&rsquo; at TU Dortmund University, two picking and one packing scenarios with 16&nbsp;subjects were recorded using an optical marker-based&nbsp;Motion Capturing system (OMoCap), Inertial Measurement Units (IMUs), and an RGB camera. Each subject was recorded for one hour (960 minutes in total).&nbsp;All the given data have been labeled and categorised into eight&nbsp;activity classes and 19&nbsp;binary coarse-semantic descriptions, also called attributes. In total, the dataset contains 221&nbsp;unique attribute representations.</p>\n\n<p>You can find the latest version of the annotation tool here:&nbsp;<a href=\"https://github.com/wilfer9008/Annotation_Tool_LARa\">https://github.com/wilfer9008/Annotation_Tool_LARa</a></p>\n\n<p><strong>Upgrade:</strong></p>\n\n<ul>\n\t<li>Subject 15 and 16 added</li>\n\t<li>OMoCap raw data added (c3d, csv)</li>\n\t<li>Second IMU set added (MotionMiners Sensors)</li>\n\t<li>OMoCap data: file names from subject 01 to subject 06 corrected</li>\n\t<li>OMoCap data: additional annotated data added</li>\n\t<li>OMoCap and IMU data (Mbientlab and MotionMiners Sensors): Annotation errors corrected</li>\n\t<li>OMoCap Networks added (all for Window Size of 200 frames (1sec.))&nbsp;\n\t<ul>\n\t\t<li>tCNN_classes</li>\n\t\t<li>tCNN-IMU_classes</li>\n\t\t<li>tCNN_attrib</li>\n\t\t<li>tCNN-IMU_attrib&nbsp;</li>\n\t</ul>\n\t</li>\n\t<li>Mbientlab Networks added (all for Window Size of 100 frames&nbsp;(1sec.))\n\t<ul>\n\t\t<li>tCNN_classes</li>\n\t\t<li>tCNN-IMU_classes</li>\n\t\t<li>tCNN_attrib</li>\n\t\t<li>tCNN-IMU_attrib</li>\n\t</ul>\n\t</li>\n\t<li>Protocol extended (now README file)</li>\n\t<li>List of unique attribute representations added (csv)</li>\n</ul>\n\n<p>&nbsp;</p>\n\n<p><strong>If you use this dataset&nbsp;for research, please&nbsp;cite the following paper: &ldquo;LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes</strong><strong>&rdquo;,&nbsp;Sensors&nbsp;2020,&nbsp;DOI:&nbsp;<a href=\"https://doi.org/10.3390/s20154083\">10.3390/s20154083</a>.</strong></p>\n\n<p><strong>If you use the Mbientlab Networks, please cite the following paper: &ldquo;From Human Pose to On-Body Devices for Human-Activity Recognition&rdquo;,&nbsp;25th International Conference on Pattern Recognition (ICPR), 2021, DOI: </strong><a href=\"https://doi.org/10.1109/ICPR48806.2021.9412283\"><strong>10.1109/ICPR48806.2021.9412283</strong></a><strong>.</strong></p>\n\n<p>If you have any questions about the dataset, please contact&nbsp;<strong><a href=\"mailto:friedrich.niemann@tu-dortmund.de\">friedrich.niemann@tu-dortmund.de</a>.</strong></p>", 
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    "title": "Logistic Activity Recognition Challenge (LARa Version 02) \u2013 A Motion Capture and Inertial Measurement Dataset", 
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    "notes": "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\".", 
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