Radar signal processing for sensing in assisted living: the challenges associated with real-time implementation of emerging algorithms

Le Kernec, J. , Fioranelli, F. , Ding, C., Zhao, H., Sun, L., Hong, H., Lorandel, J. and Romain, O. (2019) Radar signal processing for sensing in assisted living: the challenges associated with real-time implementation of emerging algorithms. IEEE Signal Processing Magazine, 36(4), pp. 29-41. (doi:10.1109/MSP.2019.2903715)

[img]
Preview
Text
181158.pdf - Accepted Version

1MB

Abstract

This article covers radar signal processing for sensing in the context of assisted living (AL). This is presented through three example applications: human activity recognition (HAR) for activities of daily living (ADL), respiratory disorders, and sleep stages (SSs) classification. The common challenge of classification is discussed within a framework of measurements/preprocessing, feature extraction, and classification algorithms for supervised learning. Then, the specific challenges of the three applications from a signal processing standpoint are detailed in their specific data processing and ad hoc classification strategies. Here, the focus is on recent trends in the field of activity recognition (multidomain, multimodal, and fusion), health-care applications based on vital signs (superresolution techniques), and comments related to outstanding challenges. Finally, this article explores challenges associated with the real-time implementation of signal processing/classification algorithms.

Item Type:Articles
Additional Information:Special Issue on Advances in Radar Systems for Modern Civilian and Commercial Applications
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Fioranelli, Dr Francesco and Le Kernec, Dr Julien
Authors: Le Kernec, J., Fioranelli, F., Ding, C., Zhao, H., Sun, L., Hong, H., Lorandel, J., and Romain, O.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Signal Processing Magazine
Publisher:IEEE
ISSN:1053-5888
ISSN (Online):1558-0792
Published Online:26 June 2019
Copyright Holders:Copyright © 2019 IEEE
First Published:First published in IEEE Signal Processing Magazine 36(4):29-41
Publisher Policy:Reproduced in accordance with the publisher copyright policy

University Staff: Request a correction | Enlighten Editors: Update this record

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

Downloads per month over past year

View more statistics