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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    <subfield code="a">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".</subfield>
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    <subfield code="a">&lt;p&gt;&lt;strong&gt;LARa&lt;/strong&gt;&lt;strong&gt; Version 02&lt;/strong&gt;&amp;nbsp;is a freely accessible logistics-dataset for human activity recognition. In the &amp;rsquo;Innovationlab Hybrid Services in Logistics&amp;rsquo; at TU Dortmund University, two picking and one packing scenarios with 16&amp;nbsp;subjects were recorded using an optical marker-based&amp;nbsp;Motion Capturing system (OMoCap), Inertial Measurement Units (IMUs), and an RGB camera. Each subject was recorded for one hour (960 minutes in total).&amp;nbsp;All the given data have been labeled and categorised into eight&amp;nbsp;activity classes and 19&amp;nbsp;binary coarse-semantic descriptions, also called attributes. In total, the dataset contains 221&amp;nbsp;unique attribute representations.&lt;/p&gt;

&lt;p&gt;You can find the latest version of the annotation tool here:&amp;nbsp;&lt;a href="https://github.com/wilfer9008/Annotation_Tool_LARa"&gt;https://github.com/wilfer9008/Annotation_Tool_LARa&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Upgrade:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;Subject 15 and 16 added&lt;/li&gt;
	&lt;li&gt;OMoCap raw data added (c3d, csv)&lt;/li&gt;
	&lt;li&gt;Second IMU set added (MotionMiners Sensors)&lt;/li&gt;
	&lt;li&gt;OMoCap data: file names from subject 01 to subject 06 corrected&lt;/li&gt;
	&lt;li&gt;OMoCap data: additional annotated data added&lt;/li&gt;
	&lt;li&gt;OMoCap and IMU data (Mbientlab and MotionMiners Sensors): Annotation errors corrected&lt;/li&gt;
	&lt;li&gt;OMoCap Networks added (all for Window Size of 200 frames (1sec.))&amp;nbsp;
	&lt;ul&gt;
		&lt;li&gt;tCNN_classes&lt;/li&gt;
		&lt;li&gt;tCNN-IMU_classes&lt;/li&gt;
		&lt;li&gt;tCNN_attrib&lt;/li&gt;
		&lt;li&gt;tCNN-IMU_attrib&amp;nbsp;&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
	&lt;li&gt;Mbientlab Networks added (all for Window Size of 100 frames&amp;nbsp;(1sec.))
	&lt;ul&gt;
		&lt;li&gt;tCNN_classes&lt;/li&gt;
		&lt;li&gt;tCNN-IMU_classes&lt;/li&gt;
		&lt;li&gt;tCNN_attrib&lt;/li&gt;
		&lt;li&gt;tCNN-IMU_attrib&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
	&lt;li&gt;Protocol extended (now README file)&lt;/li&gt;
	&lt;li&gt;List of unique attribute representations added (csv)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;If you use this dataset&amp;nbsp;for research, please&amp;nbsp;cite the following paper: &amp;ldquo;LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes&lt;/strong&gt;&lt;strong&gt;&amp;rdquo;,&amp;nbsp;Sensors&amp;nbsp;2020,&amp;nbsp;DOI:&amp;nbsp;&lt;a href="https://doi.org/10.3390/s20154083"&gt;10.3390/s20154083&lt;/a&gt;.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;If you use the Mbientlab Networks, please cite the following paper: &amp;ldquo;From Human Pose to On-Body Devices for Human-Activity Recognition&amp;rdquo;,&amp;nbsp;25th International Conference on Pattern Recognition (ICPR), 2021, DOI: &lt;/strong&gt;&lt;a href="https://doi.org/10.1109/ICPR48806.2021.9412283"&gt;&lt;strong&gt;10.1109/ICPR48806.2021.9412283&lt;/strong&gt;&lt;/a&gt;&lt;strong&gt;.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;If you have any questions about the dataset, please contact&amp;nbsp;&lt;strong&gt;&lt;a href="mailto:friedrich.niemann@tu-dortmund.de"&gt;friedrich.niemann@tu-dortmund.de&lt;/a&gt;.&lt;/strong&gt;&lt;/p&gt;</subfield>
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