Human Activity Classification With Radar Signal Processing and Machine Learning

Jia, M., Li, S., Le Kernec, J. , Yang, S. , Fioranelli, F. and Romain, O. (2020) Human Activity Classification With Radar Signal Processing and Machine Learning. In: 5th International Conference on the UK-China Emerging Technologies (UCET 2020), Glasgow, UK, 20-21 Aug 2020, ISBN 9781728194882 (doi: 10.1109/UCET51115.2020.9205461)

Abstract

As the number of older adults increases worldwide, new paradigms for indoor activity monitoring are required to keep people living at home independently longer. Radar-based human activity recognition has been identified as a sensing modality of choice because it is privacy-preserving and does not require end-users compliance or manipulation. In this paper, we explore the robustness of machine learning algorithms for human activity recognition using six different activities from the University of Glasgow dataset recorded with an FMCW radar. The raw radar data is pre-processed and represented using four different domains, namely, range-time, range-Doppler amplitude and phase diagrams, and Cadence Velocity Diagram. From those, salient features can be extracted and classified using Support Vector Machine, Stacked AutoEncoder, and Convolutional Neural Networks. The fusion of handcrafted features and features from CNN is applied to get the best scheme of classification with over 96% accuracy.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Romain, Professor Olivier and Fioranelli, Dr Francesco and Yang, Dr Shufan and Le Kernec, Dr Julien
• Authors: Jia, M., Li, S., Le Kernec, J., Yang, S., Fioranelli, F., and Romain, O.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISBN: 9781728194882
• Copyright Holders: Copyright © 2020 IEEE
• First Published: First published in 2020 International Conference on UK-China Emerging Technologies (UCET)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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