Activity Classification Using Raw Range and I & Q Radar Data with Long Short Term Memory Layers

Loukas, C., Fioranelli, F. , Le Kernec, J. and Yang, S. (2018) Activity Classification Using Raw Range and I & Q Radar Data with Long Short Term Memory Layers. In: 1st International Workshop on Healthcare with Intelligent Sensing, System and Data (HISSD) in CyberSciTech2018, Athens, Greece, 12-15 Aug 2018, pp. 441-445. ISBN 9781538675199 (doi: 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00088)

164293.pdf - Accepted Version



This paper presents the first initial results of using radar raw I & Q data and range profiles combined with Long Short Term Memory layers to classify human activities. Although tested only on simple classification problems, this is an innovative approach that enables to bypass the conventional usage of Doppler-time patterns (spectrograms) as inputs of the Long Short Term Memory layers, and adopt instead sequences of range profiles or even raw complex data as inputs. A maximum 99.56% accuracy and a mean accuracy of 97.67% was achieved by treating the radar data as these time sequences, in an effective scheme using a deep learning approach that did not require the pre-processing of the radar data to generate spectrograms and treat them as images. The prediction time needed for a given input testing sample is also reported, showing a promising path for real-time implementation once the Long Short Term Memory layers network is properly trained.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Loukas, Charalampos and Yang, Dr Shufan and Fioranelli, Dr Francesco and Le Kernec, Dr Julien
Authors: Loukas, C., Fioranelli, F., Le Kernec, J., and Yang, S.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Copyright Holders:Copyright © 2018 IEEE
First Published:First published in 2018 IEEE 16th Int. Conf. on Dependable, Autonomic and Secure Comp., 16th Int. Conf. on Pervasive Intelligence and Comp., 4th Int. Conf. on Big Data Intelligence and Comp., and 3rd Cyber Sci. and Tech. Cong. 441-445
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3015260Intelligent RF Sensing for Fall and Health PredictionFrancesco FioranelliEngineering and Physical Sciences Research Council (EPSRC)EP/R041679/1ENG - Systems Power & Energy