Elderly Care: Using Deep Learning for Multi-Domain Activity Classification

Li, S., Jia, M., Le Kernec, J. , Yang, S. , Fioranelli, F. and Romain, O. (2020) Elderly Care: Using Deep Learning for Multi-Domain Activity Classification. In: 5th International Conference on the UK-China Emerging Technologies (UCET 2020), Glasgow, UK, 20-21 Aug 2020, ISBN 9781728194882 (doi: 10.1109/UCET51115.2020.9205464)

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221497.pdf - Accepted Version



Nowadays, health monitoring issues are increasing as the worldwide population is aging. In this paper, the radar modality is used to classify with radar signature automatically. The classic approach is to extract features from micro-Doppler signatures for classification. This data representation domain has its limitations for activities presenting similar accelerations like a frontal fall and picking up an object from the floor that lead to wrongly labeled activities. In this work, we propose to combine multiple radar data domains with deep learning. Features are extracted from four domains, namely, Range-Time, Range-Doppler, Doppler-Time, and Cadence Velocity Diagram. The extracted features are set as the input of a Convolutional Neural Network, yielding 91% accuracy with 10-fold cross-validation based on the University of Glasgow “Radar signatures of human activities” open dataset.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Romain, Professor Olivier and Fioranelli, Dr Francesco and Yang, Dr Shufan and Le Kernec, Dr Julien
Authors: Li, S., Jia, M., 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
Copyright Holders:Copyright © 2020 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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