Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

DaRA Dataset: Combining Wearable Sensors, Location Tracking, and Process Knowledge for Enhanced Human Activity and Human Context Recognition in Warehousing.

Sensors (Basel, Switzerland)·2026
Same author

Context-Aware Human Activity Recognition in Industrial Processes.

Sensors (Basel, Switzerland)·2022
See all related articles

Related Experiment Video

Updated: Jul 1, 2026

Differentiation of a Human Neural Stem Cell Line on Three Dimensional Cultures, Analysis of MicroRNA and Putative Target Genes
10:48

Differentiation of a Human Neural Stem Cell Line on Three Dimensional Cultures, Analysis of MicroRNA and Putative Target Genes

Published on: April 12, 2015

10.4K

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

Friedrich Niemann1, Christopher Reining1, Fernando Moya Rueda2

  • 1Chair of Materials Handling and Warehousing, TU Dortmund University, Joseph-von-Fraunhofer-Str. 2-4, 44227 Dortmund, Germany.

Sensors (Basel, Switzerland)
|July 26, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces the first public dataset for human activity recognition (HAR) in logistics. It enables advanced deep learning models for optimizing warehouse operations.

Keywords:
attribute-based representationdatasethuman activity recognitioninertial measurement unitlogisticsmotion capturing

More Related Videos

Simplified, High-throughput Analysis of Single-cell Contractility using Micropatterned Elastomers
14:33

Simplified, High-throughput Analysis of Single-cell Contractility using Micropatterned Elastomers

Published on: April 8, 2022

3.9K
Author Spotlight: Analysis of Ovarian Anatomy in Migratory Insects to Overcome Experimental Challenges
04:41

Author Spotlight: Analysis of Ovarian Anatomy in Migratory Insects to Overcome Experimental Challenges

Published on: July 14, 2023

2.0K

Related Experiment Videos

Last Updated: Jul 1, 2026

Differentiation of a Human Neural Stem Cell Line on Three Dimensional Cultures, Analysis of MicroRNA and Putative Target Genes
10:48

Differentiation of a Human Neural Stem Cell Line on Three Dimensional Cultures, Analysis of MicroRNA and Putative Target Genes

Published on: April 12, 2015

10.4K
Simplified, High-throughput Analysis of Single-cell Contractility using Micropatterned Elastomers
14:33

Simplified, High-throughput Analysis of Single-cell Contractility using Micropatterned Elastomers

Published on: April 8, 2022

3.9K
Author Spotlight: Analysis of Ovarian Anatomy in Migratory Insects to Overcome Experimental Challenges
04:41

Author Spotlight: Analysis of Ovarian Anatomy in Migratory Insects to Overcome Experimental Challenges

Published on: July 14, 2023

2.0K

Area of Science:

  • Logistics and Supply Chain Management
  • Computer Science
  • Human-Computer Interaction

Background:

  • Optimizing logistics operations necessitates accurate human activity recognition (HAR).
  • Existing HAR datasets do not adequately represent logistics environments.
  • Sensor-based HAR in logistics remains an underexplored research area.

Purpose of the Study:

  • To introduce the first publicly available dataset specifically designed for human activity recognition in logistics.
  • To provide a comprehensive resource for developing and evaluating HAR models in warehouse settings.
  • To facilitate advancements in sensor-based HAR for logistics applications.

Main Methods:

  • Recreation of picking and packing scenarios within an 'Innovationlab Hybrid Services in Logistics'.
  • Data collection from 14 subjects using Optical marker-based Motion Capture (OMoCap), inertial measurement units (IMUs), and an RGB camera.
  • Labeling of 758 minutes of recordings into 8 activity classes and 19 attributes by 12 annotators.

Main Results:

  • The creation of a novel, freely accessible logistics-focused HAR dataset.
  • Comprehensive labeling of activities and attributes, enabling detailed analysis.
  • The dataset is prepared for training deep learning models for logistics HAR.

Conclusions:

  • The developed dataset addresses a critical gap in available resources for logistics HAR research.
  • This dataset will accelerate the development of sophisticated HAR systems for warehouse environments.
  • It supports the application of deep learning techniques to enhance logistics efficiency through activity recognition.