# Unsupervised Doppler Radar-Based Activity Recognition for e-Healthcare

arXiv: https://arxiv.org/abs/2103.10478 · HTML: https://mlsanity.com/paper/2103.10478/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 73 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Anomaly Detection using Autoencoders in High Performance Computing Systems](https://mlsanity.com/paper/1811.05269/) (2018)
- [Adversarially Approximated Autoencoder for Image Generation and Manipulation](https://mlsanity.com/paper/1902.05581/) (2019)
- [The Dreaming Variational Autoencoder for Reinforcement Learning Environments](https://mlsanity.com/paper/1810.01112/) (2018)
- [Motion Classification using Kinematically Sifted ACGAN-Synthesized Radar Micro-Doppler Signatures](https://mlsanity.com/paper/2001.08582/) (2020)

## Abstract (arXiv)

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.