mmPrivPose3D: A dataset for pose estimation and gesture command recognition in human-robot collaboration using frequency modulated continuous wave 60Hhz RaDARMendeley Data
3D pose estimation and gesture command recognition are crucial for ensuring safety and improving human-robot interaction. While RGB-D cameras are commonly used for these tasks, they often raise privacy concerns due to their ability to capture detailed visual data of human operators. In contrast, usi...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000484 |
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| author | Nima Roshandel Constantin Scholz Hoang-Long Cao Milan Amighi Hamed Firouzipouyaei Aleksander Burkiewicz Sebastien Menet Felipe Ballen-Moreno Dylan Warawout Sisavath Emil Imrith Antonio Paolillo Jan Genoe Bram Vanderborght |
| author_facet | Nima Roshandel Constantin Scholz Hoang-Long Cao Milan Amighi Hamed Firouzipouyaei Aleksander Burkiewicz Sebastien Menet Felipe Ballen-Moreno Dylan Warawout Sisavath Emil Imrith Antonio Paolillo Jan Genoe Bram Vanderborght |
| author_sort | Nima Roshandel |
| collection | DOAJ |
| description | 3D pose estimation and gesture command recognition are crucial for ensuring safety and improving human-robot interaction. While RGB-D cameras are commonly used for these tasks, they often raise privacy concerns due to their ability to capture detailed visual data of human operators. In contrast, using RaDAR sensors offers a privacy-preserving alternative, as they can output point-cloud data rather than images. We introduce mmPrivPose3D, a dataset of 3D RaDAR point-cloud data that captures human movements and gestures using a single IWR6843AOPEVM RaDAR sensor with a frequency of 10 Hz synchronized with 19 corresponding 3D skeleton keypoints as the ground truth. These keypoints were extracted from RGB-D images captured by an Intel RealSense camera recorded at 30 frames per second using the Nuitrack SDK, and labeled with gestures. The dataset was collected from n = 15 participants. Our dataset serves as a fundamental resource for developing machine learning algorithms to improve the accuracy of pose estimation and gesture recognition using RaDAR data. |
| format | Article |
| id | doaj-art-75da30526f3a4816953d58aad730bb5d |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-75da30526f3a4816953d58aad730bb5d2025-01-26T05:04:05ZengElsevierData in Brief2352-34092025-04-0159111316mmPrivPose3D: A dataset for pose estimation and gesture command recognition in human-robot collaboration using frequency modulated continuous wave 60Hhz RaDARMendeley DataNima Roshandel0Constantin Scholz1Hoang-Long Cao2Milan Amighi3Hamed Firouzipouyaei4Aleksander Burkiewicz5Sebastien Menet6Felipe Ballen-Moreno7Dylan Warawout Sisavath8Emil Imrith9Antonio Paolillo10Jan Genoe11Bram Vanderborght12Brubotics, Vrije Universiteit Brussel, Brussels, Belgium; imec-IMS-VUB, Belgium; KU Leuven, Belgium; Corresponding authors.Brubotics, Vrije Universiteit Brussel, Brussels, Belgium; imec-IMS-VUB, BelgiumBrubotics, Vrije Universiteit Brussel, Brussels, Belgium; Flanders Make, Brussels, Belgium; Corresponding authors.Brubotics, Vrije Universiteit Brussel, Brussels, Belgium; imec-IMS-VUB, BelgiumBrubotics, Vrije Universiteit Brussel, Brussels, Belgium; imec-IMS-VUB, BelgiumBrubotics, Vrije Universiteit Brussel, Brussels, Belgium; imec-IMS-VUB, BelgiumBrubotics, Vrije Universiteit Brussel, Brussels, Belgium; imec-IMS-VUB, BelgiumBrubotics, Vrije Universiteit Brussel, Brussels, Belgium; Flanders Make, Brussels, BelgiumBrubotics, Vrije Universiteit Brussel, Brussels, Belgium; imec-IMS-VUB, BelgiumBrubotics, Vrije Universiteit Brussel, Brussels, BelgiumBrubotics, Vrije Universiteit Brussel, Brussels, Belgium; SOFT Languages Lab, Vrije Universiteit Brussel, Belgiumimec-SAT, Belgium; KU Leuven, BelgiumBrubotics, Vrije Universiteit Brussel, Brussels, Belgium; KU Leuven, Belgium3D pose estimation and gesture command recognition are crucial for ensuring safety and improving human-robot interaction. While RGB-D cameras are commonly used for these tasks, they often raise privacy concerns due to their ability to capture detailed visual data of human operators. In contrast, using RaDAR sensors offers a privacy-preserving alternative, as they can output point-cloud data rather than images. We introduce mmPrivPose3D, a dataset of 3D RaDAR point-cloud data that captures human movements and gestures using a single IWR6843AOPEVM RaDAR sensor with a frequency of 10 Hz synchronized with 19 corresponding 3D skeleton keypoints as the ground truth. These keypoints were extracted from RGB-D images captured by an Intel RealSense camera recorded at 30 frames per second using the Nuitrack SDK, and labeled with gestures. The dataset was collected from n = 15 participants. Our dataset serves as a fundamental resource for developing machine learning algorithms to improve the accuracy of pose estimation and gesture recognition using RaDAR data.http://www.sciencedirect.com/science/article/pii/S2352340925000484Human-robot collaborationIWR6843AOPEVMRaDARPose estimationGesture command recognition |
| spellingShingle | Nima Roshandel Constantin Scholz Hoang-Long Cao Milan Amighi Hamed Firouzipouyaei Aleksander Burkiewicz Sebastien Menet Felipe Ballen-Moreno Dylan Warawout Sisavath Emil Imrith Antonio Paolillo Jan Genoe Bram Vanderborght mmPrivPose3D: A dataset for pose estimation and gesture command recognition in human-robot collaboration using frequency modulated continuous wave 60Hhz RaDARMendeley Data Data in Brief Human-robot collaboration IWR6843AOPEVM RaDAR Pose estimation Gesture command recognition |
| title | mmPrivPose3D: A dataset for pose estimation and gesture command recognition in human-robot collaboration using frequency modulated continuous wave 60Hhz RaDARMendeley Data |
| title_full | mmPrivPose3D: A dataset for pose estimation and gesture command recognition in human-robot collaboration using frequency modulated continuous wave 60Hhz RaDARMendeley Data |
| title_fullStr | mmPrivPose3D: A dataset for pose estimation and gesture command recognition in human-robot collaboration using frequency modulated continuous wave 60Hhz RaDARMendeley Data |
| title_full_unstemmed | mmPrivPose3D: A dataset for pose estimation and gesture command recognition in human-robot collaboration using frequency modulated continuous wave 60Hhz RaDARMendeley Data |
| title_short | mmPrivPose3D: A dataset for pose estimation and gesture command recognition in human-robot collaboration using frequency modulated continuous wave 60Hhz RaDARMendeley Data |
| title_sort | mmprivpose3d a dataset for pose estimation and gesture command recognition in human robot collaboration using frequency modulated continuous wave 60hhz radarmendeley data |
| topic | Human-robot collaboration IWR6843AOPEVM RaDAR Pose estimation Gesture command recognition |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925000484 |
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