DOI: 10.5281/zenodo.8189341
title: Logistic Activity Recognition Challenge (LARa Version 03) – A Motion Capture and Inertial Measurement Dataset

description: <strong>LARa</strong><strong> Version 03</strong> is a freely accessible logistics-dataset for human activity recognition. In the “Innovationlab Hybrid Services in Logistics” at TU Dortmund University, two picking and one packing scenarios with 16 subjects were recorded using an optical marker-based Motion Capturing system (OMoCap), Inertial Measurement Units (IMUs), and an RGB camera. Each subject was recorded for one hour (960 minutes in total). All the given data have been labelled and categorised into eight activity classes and 19 binary coarse-semantic descriptions, also called attributes. In total, the dataset contains 221 unique attribute representations. The <strong>dataset was created according to the guideline</strong> of the following paper: “A Tutorial on Dataset Creation for Sensor-based Human Activity Recognition”, PerCom, 2023 DOI: 10.1109/PerComWorkshops56833.2023.10150401 The LARa Version 03 contains a <strong>new Annotation tool </strong>for OMoCap and RGB Videos, namely, the <strong>S</strong>equence <strong>A</strong>ttribute <strong>R</strong>etrieval <strong>A</strong>nnotator (<strong>SARA</strong>). SARA, developed and modified based on the LARa Version 02 annotation tool, includes desirable features and attempts to overcome limitations as found in the LARa annotation tool. Furthermore, few features were included based on the explorative study of previously developed annotation tools, see journal. In alignment with the LARa annotation tool, SARA focuses on OMoCap and video annotations. However, it is to be noted that SARA was not intended to be a video annotation tool with features such as subject tracking and multiple subject annotations. Here, the video is considered to be a supporting input to the OMoCap annotation. We would recommend other tools for pure video-based multiple-human activity annotation, including subject tracking, segmentation, and pose estimation. There are different ways of <strong>installing the annotation tool</strong>: Compiled binaries (executable files) for Windows and Mac can be directly downloaded from here. Python users can install the tool from https://pypi.org/project/annotation-tool/ (PyPi): “pip install annotation-tool”. For more information, please refer to the “Annotation Tool - Installation and User Manual”. <strong>Upgrade:</strong> Annotation tool (<strong>SARA</strong>) added (for Windows and MacOS, including an installation and user manual) Neural Networks updated (can be used with the annotation tool) OMoCap data: Annotation errors corrected Annotations reformatted, fitting the SARA annotation tool “additional annotated data” extended “Markers_Exports” added IMU data (MbientLab and MotionMiners Sensors) Annotation errors corrected README file (protocol) updated and extended <strong>If you use this dataset for research, please cite the following paper: “LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes</strong><strong>”, Sensors 2020, DOI: 10.3390/s20154083.</strong> <strong>If you use the Mbientlab Networks, please cite the following paper: “From Human Pose to On-Body Devices for Human-Activity Recognition”, 25th International Conference on Pattern Recognition (ICPR), 2021, DOI: </strong><strong>10.1109/ICPR48806.2021.9412283</strong><strong>.</strong> For any questions about the dataset, please contact Friedrich Niemann at friedrich.niemann@tu-dortmund.de.
description: 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".