FMCW radar and inertial sensing synergy for assisted living

Li, H., Shrestha, A., Fioranelli, F. , Le Kernec, J. and Heidari, H. (2019) FMCW radar and inertial sensing synergy for assisted living. Journal of Engineering, 2019(20), pp. 6784-6789. (doi: 10.1049/joe.2019.0558)

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This study presents preliminary results about the multi-sensory recognition of indoor daily activities and fall detection, to monitor the well-being of older people at risk of physical and cognitive chronic health conditions. Five different sensors, continuous wave (CW) radar, frequency-modulated CW (FMCW) radar, and inertial measurement unit comprising an accelerometer, gyroscope, and magnetometer were used to simultaneously collect data from 20 subjects performing 10 activities. Rather than using all of the available sensors, it is more efficient and economical to select part of them to maximise the classification accuracy and avoid unnecessary computation to process information if it is not salient. Each individual sensor and several sensor combinations are trained with a quadratic-kernel support vector machine classifier. In addition, they are validated with an improved statistical approach, which uses data from unknown participants to test model rather than random cross-validation to verify if the model generalises well for unknown subjects. Furthermore, the most suitable sensor combinations are derived for each specific group of tested subjects selected (e.g. the oldest, youngest, tallest, and shortest sub-groups of participants out of the entire group).

Item Type:Articles
Additional Information:The authors acknowledge support from the UK EPSRC through grant EP/R041679/1 INSHEP, and Doctoral Training Award supporting A. Shrestha in his PhD at the School of Engineering, University of Glasgow.
Glasgow Author(s) Enlighten ID:Fioranelli, Dr Francesco and Heidari, Dr Hadi and Le Kernec, Dr Julien and Shrestha, Mr Aman and Li, Haobo
Authors: Li, H., Shrestha, A., Fioranelli, F., Le Kernec, J., and Heidari, H.
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:Journal of Engineering
ISSN (Online):2051-3305
Published Online:11 July 2019
Copyright Holders:Copyright © Institution of Engineering and Technology 2019
First Published:First published in Journal of Engineering 2019(20):6784-6789
Publisher Policy:Reproduced under a Creative Commons license

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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