Roshandel, N., Scholz, C., Cao, H.-L., Cao, H.-G., Amighi, M., Firouzipouyaei, H., Burkiewicz, A., Menet, S., Ballen-Moreno, F., Sisavath, D. W., Imrith, E., Paolillo, A., Genoe, J., & Vanderborght, B. (2025). mmPrivPose3D: A RaDAR-based approach to privacy-compliant pose estimation and gesture command recognition in human-robot collaboration. IEEE Sensors Journal, 25(15), 29437-29445. https://doi.org/10.1109/JSEN.2025.3578094

@article{d1f5df8578f64acdbff2786ece8fc295,
title = "mmPrivPose3D: A RaDAR-based approach to privacy-compliant pose estimation and gesture command recognition in human-robot collaboration",
abstract = "Various sensors are employed in dynamic human-robot collaboration manufacturing environments for real-time human pose estimation to improve safety through collision-avoidance systems and gesture command recognition to enhance human-robot interaction. However, the most widely used sensors – RGBD cameras – often underperform under varying lighting and environmental conditions and raise privacy concerns. This paper introduces mmPrivPose3D, a novel system designed to prioritize privacy while performing human pose estimation and gesture command recognition using a 60 GHz industrial Frequency Modulated Continuous Wave (FMCW) RaDAR with a 10 m maximum range and 29 degrees angular resolution. The system employs a parallel architecture including a 3D Convolutional Neural Network (CNN) for pose estimation, which extracts 19 keypoints of the human skeleton, along with a random forest classifier for recognizing gesture commands. The system was trained on a dataset involving ten individuals performing various movements in a human-robot interaction context, including walking in the workspace and hand-waving gestures. Our model demonstrated a low Mean Per Joint Position Error (MPJPE) of 4.8% across keypoints for pose estimation and, for gesture recognition, an accuracy of 96.3% during k-fold cross-validation and 96.2% during inference. mmPrivPose3D has the potential for application in human workspace localization and human-to-robot communication, particularly in contexts where privacy is a concern.",
keywords = "human-robot collaboration, RaDAR, pose estimation, gesture command recognition, deep learning, privacy",
author = "Nima Roshandel and Constantin Scholz and Hoang-Long Cao and Hoang-Giang Cao and Milan Amighi and Hamed Firouzipouyaei and Aleksander Burkiewicz and Sebastien Menet and Felipe Ballen-Moreno and Sisavath, {Dylan Warawout} and Emil Imrith and Antonio Paolillo and Jan Genoe and Bram Vanderborght",
note = "Publisher Copyright: {\textcopyright} 2001-2012 IEEE.",
year = "2025",
month = jun,
day = "16",
doi = "10.1109/JSEN.2025.3578094",
language = "English",
volume = "25",
pages = "29437--29445",
journal = "IEEE Sensors Journal",
issn = "1530-437X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "15",
}