Tang, Chong;
Li, Wenda;
Vishwakarma, Shelly;
Shi, Fangzhan;
Julier, Simon;
Chetty, Kevin;
(2023)
MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler
Signatures.
IEEE Transactions on Aerospace and Electronic Systems
10.1109/TAES.2023.3256973.
(In press).
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Abstract
Motion tracking systems based on optical sensors typically often suffer from issues, such as poor lighting conditions, occlusion, limited coverage, and may raise privacy concerns. More recently, radio frequency (RF)-based approaches using commercial WiFi devices have emerged which offer low-cost ubiquitous sensing whilst preserving privacy. However, the output of an RF sensing system, such as Range-Doppler spectrograms, cannot represent human motion intuitively and usually requires further processing. In this study, MDPose, a novel framework for human skeletal motion reconstruction based on WiFi micro-Doppler signatures, is proposed. It provides an effective solution to track human activities by reconstructing a skeleton model with 17 key points, which can assist with the interpretation of conventional RF sensing outputs in a more understandable way. Specifically, MDPose has various incremental stages to gradually address a series of challenges: First, a denoising algorithm is implemented to remove any unwanted noise that may affect the feature extraction and enhance weak Doppler signatures. Secondly, the convolutional neural network (CNN)-recurrent neural network (RNN) architecture is applied to learn temporal-spatial dependency from clean micro-Doppler signatures and restore key points' velocity information. Finally, a pose optimising mechanism is employed to estimate the initial state of the skeleton and to limit the increase of error. We have conducted comprehensive tests in a variety of environments using numerous subjects with a single receiver radar system to demonstrate the performance of MDPose, and report 29.4mm mean absolute error over all key points positions, which outperforms state-of-the-art RF-based pose estimation systems.
| Type: | Article |
|---|---|
| Title: | MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TAES.2023.3256973 |
| Publisher version: | https://doi.org/10.1109/TAES.2023.3256973 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10166224 |
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