MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures

• URL: http://arxiv.org/abs/2201.04212v1
• Date: Tue, 11 Jan 2022 21:46:28 GMT
• Title: MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures
• Authors: Chong Tang, Wenda Li, Shelly Vishwakarma, Fangzhan Shi, Simon Julier, Kevin Chetty
• Abstract summary: We propose MDPose, a novel framework for human skeletal motion reconstruction based on WiFi micro-Doppler signatures. It provides an effective solution to track human activities by reconstructing a skeleton model with 17 key points. MDPose outperforms state-of-the-art RF-based pose estimation systems.
• Score: 4.92674421365689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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