LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes
Optimizations in logistics require recognition and analysis of human activities. The potential of sensor-based human activity recognition (HAR) in logistics is not yet well explored. Despite a significant increase in HAR datasets in the past twenty years, no available dataset depicts activities in l...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/15/4083 |
| _version_ | 1797561642456711168 |
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| author | Friedrich Niemann Christopher Reining Fernando Moya Rueda Nilah Ravi Nair Janine Anika Steffens Gernot A. Fink Michael ten Hompel |
| author_facet | Friedrich Niemann Christopher Reining Fernando Moya Rueda Nilah Ravi Nair Janine Anika Steffens Gernot A. Fink Michael ten Hompel |
| author_sort | Friedrich Niemann |
| collection | DOAJ |
| description | Optimizations in logistics require recognition and analysis of human activities. The potential of sensor-based human activity recognition (HAR) in logistics is not yet well explored. Despite a significant increase in HAR datasets in the past twenty years, no available dataset depicts activities in logistics. This contribution presents the first freely accessible logistics-dataset. In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios were recreated. Fourteen subjects were recorded individually when performing warehousing activities using Optical marker-based Motion Capture (OMoCap), inertial measurement units (IMUs), and an RGB camera. A total of 758 min of recordings were labeled by 12 annotators in 474 person-h. All the given data have been labeled and categorized into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. The dataset is deployed for solving HAR using deep networks. |
| first_indexed | 2024-03-10T18:17:48Z |
| format | Article |
| id | doaj.art-4cbc4dd0499a419f900787c6e5306d5b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:17:48Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4cbc4dd0499a419f900787c6e5306d5b2023-11-20T07:35:20ZengMDPI AGSensors1424-82202020-07-012015408310.3390/s20154083LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic AttributesFriedrich Niemann0Christopher Reining1Fernando Moya Rueda2Nilah Ravi Nair3Janine Anika Steffens4Gernot A. Fink5Michael ten Hompel6Chair of Materials Handling and Warehousing, TU Dortmund University, Joseph-von-Fraunhofer-Str. 2-4, 44227 Dortmund, GermanyChair of Materials Handling and Warehousing, TU Dortmund University, Joseph-von-Fraunhofer-Str. 2-4, 44227 Dortmund, GermanyPattern Recognition in Embedded Systems Groups, TU Dortmund University, Otto-Hahn-Str. 16, 44227 Dortmund, GermanyChair of Materials Handling and Warehousing, TU Dortmund University, Joseph-von-Fraunhofer-Str. 2-4, 44227 Dortmund, GermanyChair of Materials Handling and Warehousing, TU Dortmund University, Joseph-von-Fraunhofer-Str. 2-4, 44227 Dortmund, GermanyPattern Recognition in Embedded Systems Groups, TU Dortmund University, Otto-Hahn-Str. 16, 44227 Dortmund, GermanyChair of Materials Handling and Warehousing, TU Dortmund University, Joseph-von-Fraunhofer-Str. 2-4, 44227 Dortmund, GermanyOptimizations in logistics require recognition and analysis of human activities. The potential of sensor-based human activity recognition (HAR) in logistics is not yet well explored. Despite a significant increase in HAR datasets in the past twenty years, no available dataset depicts activities in logistics. This contribution presents the first freely accessible logistics-dataset. In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios were recreated. Fourteen subjects were recorded individually when performing warehousing activities using Optical marker-based Motion Capture (OMoCap), inertial measurement units (IMUs), and an RGB camera. A total of 758 min of recordings were labeled by 12 annotators in 474 person-h. All the given data have been labeled and categorized into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. The dataset is deployed for solving HAR using deep networks.https://www.mdpi.com/1424-8220/20/15/4083human activity recognitionattribute-based representationdatasetmotion capturinginertial measurement unitlogistics |
| spellingShingle | Friedrich Niemann Christopher Reining Fernando Moya Rueda Nilah Ravi Nair Janine Anika Steffens Gernot A. Fink Michael ten Hompel LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes Sensors human activity recognition attribute-based representation dataset motion capturing inertial measurement unit logistics |
| title | LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes |
| title_full | LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes |
| title_fullStr | LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes |
| title_full_unstemmed | LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes |
| title_short | LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes |
| title_sort | lara creating a dataset for human activity recognition in logistics using semantic attributes |
| topic | human activity recognition attribute-based representation dataset motion capturing inertial measurement unit logistics |
| url | https://www.mdpi.com/1424-8220/20/15/4083 |
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