mmPrivPose3D: A dataset for pose estimation and gesture command recognition in human-robot collaboration using frequency modulated continuous wave 60Hhz RaDAR

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. (c) 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/)