Unsupervised Doppler Radar-Based Activity Recognition for e-Healthcare

TL;DR

This paper introduces new unsupervised feature extraction methods, including DCT, entropy, and CVAE, for Doppler radar-based activity recognition in elderly care, demonstrating improved accuracy and efficiency over existing techniques.

Contribution

It presents three novel unsupervised feature extraction approaches for Doppler radar data, including the first application of CVAE, and compares their performance with existing methods.

Findings

DCT, entropy, and CVAE features outperform PCA, 2DPCA, and CAE in accuracy.

Proposed methods are significantly faster than CVAE.

Manifold learning techniques improve visualization of encoded features.

Abstract

Passive radio frequency (RF) sensing and monitoring of human daily activities in elderly care homes is an emerging topic. Micro-Doppler radars are an appealing solution considering their non-intrusiveness, deep penetration, and high-distance range. Unsupervised activity recognition using Doppler radar data has not received attention, in spite of its importance in case of unlabelled or poorly labelled activities in real scenarios. This study proposes two unsupervised feature extraction methods for the purpose of human activity monitoring using Doppler-streams. These include a local Discrete Cosine Transform (DCT)-based feature extraction method and a local entropy-based feature extraction method. In addition, a novel application of Convolutional Variational Autoencoder (CVAE) feature extraction is employed for the first time for Doppler radar data. The three feature extraction architectures are compared with the previously used Convolutional Autoencoder (CAE) and linear feature extraction based on Principal Component Analysis (PCA) and 2DPCA. Unsupervised clustering is performed using K-Means and K-Medoids. The results show the superiority of DCT-based method, entropy-based method, and CVAE features compared to CAE, PCA, and 2DPCA, with more than 5\%-20\% average accuracy. In regards to computation time, the two proposed methods are noticeably much faster than the existing CVAE. Furthermore, for high-dimensional data visualisation, three manifold learning techniques are considered. The methods are compared for the projection of raw data as well as the encoded CVAE features. All three methods show an improved visualisation ability when applied to the encoded CVAE features.