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• W3044644239 endingPage "4083" @default.
• W3044644239 startingPage "4083" @default.
• W3044644239 abstract "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." @default.
• W3044644239 created "2020-07-29" @default.
• W3044644239 creator A5022250044 @default.
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• W3044644239 date "2020-07-22" @default.
• W3044644239 modified "2023-10-02" @default.
• W3044644239 title "LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes" @default.
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• W3044644239 doi "https://doi.org/10.3390/s20154083" @default.
• W3044644239 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7436169" @default.
• W3044644239 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32707928" @default.
• W3044644239 hasPublicationYear "2020" @default.
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