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

LARa Version 02 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 labeled and categorised into eight activity classes and 19 binary coarse-semantic descriptions, also called attributes. In total, the dataset contains 221 unique attribute representations.

You can find the latest version of the annotation tool here: https://github.com/wilfer9008/Annotation_Tool_LARa

Upgrade:

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

 

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”, Sensors 2020, DOI: 10.3390/s20154083.

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: 10.1109/ICPR48806.2021.9412283.

If you have any questions about the dataset, please contact friedrich.niemann@tu-dortmund.de.

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".
Files (31.4 GB)
Name Size
IMU data (annotated) _ MbientLab.zip
md5:6aecb8b8d557a2b6e61c7dfe164b28e8
627.1 MB Download
IMU data (annotated) _ MotionMiners Sensors.zip
md5:c0b77356976af6056f88f1f2445f1c73
125.4 MB Download
Networks.zip
md5:cf6f772895e1f1e30f1a7311acb475b9
6.3 GB Download
OMoCap data (additional annotated data) _ csv, txt.zip
md5:d57280072387d0643e0749818fff21f3
1.3 GB Download
OMoCap data (annotated) _ csv, txt.zip
md5:f8c8a4fbe6d9d014de18d1011becdf3e
6.9 GB Download
OMoCap data (raw) _ c3d.zip
md5:18dd9d04d78043087995e1b2583a9bdb
5.4 GB Download
OMoCap data (raw) _ csv.zip
md5:d6156911d53d34dde634187d67ce77f2
4.2 GB Download
README _ LARa Version 2.pdf
md5:955000bdc938b8ccc6888fddee3c9c7d
3.6 MB Download
RGB videos.zip
md5:814614584b7ac98a369201d3ec4cedb4
6.5 GB Download
unique attribute representations.csv
md5:e52021cbb793622dc838515e0c965812
15.3 kB Download
2,318
3,675
views
downloads
All versions This version
Views 2,318318
Downloads 3,675180
Data volume 17.0 TB554.4 GB
Unique views 1,920270
Unique downloads 74292

Share

Cite as