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

TL;DR

MDPose is a novel WiFi-based framework that reconstructs human skeletal motion using micro-Doppler signatures, overcoming challenges like noise and motion complexity to achieve accurate pose estimation without optical sensors.

Contribution

The paper introduces MDPose, a new RF-based skeletal motion reconstruction method combining denoising, deep learning, and pose optimization for improved accuracy.

Findings

Achieves 29.4mm mean absolute error in key point localization.

Outperforms existing RF-based pose estimation systems.

Effective in diverse environments with a single WiFi receiver.

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.