Magnetic and radar sensing for multimodal remote health monitoring

Li, H., Shrestha, A., Heidari, H. , Le Kernec, J. and Fioranelli, F. (2018) Magnetic and radar sensing for multimodal remote health monitoring. IEEE Sensors Journal, (doi:10.1109/JSEN.2018.2872894) (Early Online Publication)

Li, H., Shrestha, A., Heidari, H. , Le Kernec, J. and Fioranelli, F. (2018) Magnetic and radar sensing for multimodal remote health monitoring. IEEE Sensors Journal, (doi:10.1109/JSEN.2018.2872894) (Early Online Publication)

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Abstract

With the increased life expectancy and rise in health conditions related to aging, there is a need for new technologies that can routinely monitor vulnerable people, identify their daily pattern of activities and any anomaly or critical events such as falls. This paper aims to evaluate magnetic and radar sensors as suitable technologies for remote health monitoring purpose, both individually and fusing their information. After experiments and collecting data from 20 volunteers, numerical features has been extracted in both time and frequency domains. In order to analyse and verify the validation of fusion method for different classifiers, a Support Vector Machine with a quadratic kernel, and an Artificial Neural Network with one and multiple hidden layers have been implemented. Furthermore, for both classifiers, feature selection has been performed to obtain salient features. Using this technique along with fusion, both classifiers can detect 10 different activities with an accuracy rate of approximately 96%. In cases where the user is unknown to the classifier, an accuracy of approximately 92% is maintained.

Item Type:Articles
Status:Early Online Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Li, Mr Haobo and Fioranelli, Dr Francesco and Shrestha, Mr Aman and Heidari, Dr Hadi and Le Kernec, Dr Julien
Authors: Li, H., Shrestha, A., Heidari, H., Le Kernec, J., and Fioranelli, F.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Sensors Journal
Publisher:IEEE
ISSN:1530-437X
ISSN (Online):1558-1748
Published Online:08 October 2018
Copyright Holders:Copyright © 2018 IEEE
First Published:First published in IEEE Sensors Journal 2018
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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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