  Passive radio frequency (RF) sensing and monitoring of human daily activities
in elderly care homes has recently become an emerging topic due to the demand
with ageing population. Micro-Doppler radars are an appealing solution
considering their non-intrusiveness, deep penetration, and high-distance range.
This study presents an unsupervised framework for human activity monitoring
using Doppler streams. Two unsupervised feature extraction strategies based on
convolutional filtering and texture analysis of Doppler images are considered.
For the former, encoded features using Convolutional Variational Autoencoder
(CVAE) are compared with Convolutional Autoencoder (CAE) features. For the
latter, Grey-Level Co-occurrence Matrix (GLCM) is used. These methods are
further compared with unsupervised linear feature extraction based on Principal
Component Analysis (PCA) and Singular Value Decomposition (SVD). Using these
features, unsupervised samples clustering is performed using K-Means and
K-Medoids. Actual labels are solely used for evaluation and visualisation. The
results showcase 82.5% and 84% average testing accuracies for CVAE features and
77.5% and 72.5% average testing accuracy using texture features based on GLCM
using K-Means and K-Medoids respectively. The results show superiority of CVAE
and GLCM features compared to PCA, SVD, and CAE with more than 20% average
accuracy. Furthermore, for high-dimensional data visualisation, three manifold
learning techniques are considered including t-Distributed Stochastic Neighbour
Embedding (t-SNE), Multidimensional Scaling (MDS), and Locally Linear Embedding
(LLE). The visualisation methods are compared for projection of raw data as
well as the encoded features using CVAE. All three methods show an improved
visualisation ability when applied on the transformed CVAE data.
