Sequential human gait classification with distributed radar sensor fusion

Li, H., Le Kernec, J. , Gurbuz, S. Z. and Fioranelli, F. (2021) Sequential human gait classification with distributed radar sensor fusion. IEEE Sensors Journal, 21(6), pp. 7590-7603. (doi: 10.1109/JSEN.2020.3046991)

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This paper presents different information fusion approaches to classify human gait patterns and falls in a radar sensors network. The human gaits classified in this work are both individual and sequential, continuous gait collected by a FMCW radar and three UWB pulse radar placed at different spatial locations. Sequential gaits are those containing multiple gait styles performed one after the other, with natural transitions in between, including fall events developing from walking gait in some cases. The proposed information fusion approaches operate at signal and decision level. For the signal level combination, a simple trilateration algorithm is implemented on the range data from the 3 UWB radar sensors, achieving good classification results with the proposed Bi-LSTM (Bidirectional LSTM neural network) as classifier, without exploiting conventional micro-Doppler information. For the decision level fusion, the classification results of individual radars using the Bi-LSTM network are combined with a robust Naive Bayes Combiner (NBC), and this showed subsequent improvement compared to the single radar case thanks to multi-perspective views of the subjects. Compared to conventional SVM and Random Forest classifiers, the proposed approach yields +20% and +17% improvement in the classification accuracy of individual gaits for the range-only trilateration method and NBC decision fusion method, respectively. When classifying sequential gaits, the overall accuracy for the two proposed methods reaches 93% and 90%, with validation via a ’leaving one participant out’ approach to test the robustness with subjects unknown to the network.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Fioranelli, Dr Francesco and Le Kernec, Dr Julien and Li, Haobo
Authors: Li, H., Le Kernec, J., Gurbuz, S. Z., and Fioranelli, F.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Sensors Journal
ISSN (Online):1558-1748
Published Online:24 December 2020
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in IEEE Sensors Journal 21(6) 7590-7603
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
301526Intelligent RF Sensing for Fall and Health PredictionFrancesco FioranelliEngineering and Physical Sciences Research Council (EPSRC)EP/R041679/1ENG - Systems Power & Energy