Sensor and feature selection for an emergency first responders activity recognition system

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dc.contributor.author Scheurer, Sebastian
dc.contributor.author Tedesco, Salvatore
dc.contributor.author Brown, Kenneth N.
dc.contributor.author O'Flynn, Brendan
dc.date.accessioned 2018-01-31T12:30:26Z
dc.date.available 2018-01-31T12:30:26Z
dc.date.issued 2017
dc.identifier.citation Scheurer, S., Tedesco, S., Brown, K. N. and O’Flynn, B. (2017) ‘Sensor and feature selection for an emergency first responders activity recognition system’, IEEE Sensors 2017, Glasgow, Scotland, UK, 29 October – 1 November. doi:10.1109/ICSENS.2017.8234090 en
dc.identifier.isbn 978-1-5090-1012-7
dc.identifier.uri http://hdl.handle.net/10468/5355
dc.identifier.doi 10.1109/ICSENS.2017.8234090
dc.description.abstract Human activity recognition (HAR) has a wide range of applications, such as monitoring ambulatory patients’ recovery, workers for harmful movement patterns, or elderly populations for falls. These systems often operate in an environment where battery lifespan, power consumption, and hence computational complexity, are of prime concern. This work explores three methods for reducing the dimensionality of a HAR problem in the context of an emergency first responders monitoring system. We empirically estimate the accuracy of k-Nearest Neighbours, Support Vector Machines, and Gradient Boosted Trees when using different combinations of (A)ccelerometer, (G)yroscope and (P)ressure sensors. We then apply Principal Component Analysis for dimensionality reduction, and the Kruskal-Wallis test for feature selection. Our results show that the best combination is that which includes all three sensors (MAE: 3.6%), followed by the A/G (MAE: 3.7%), and the A/P combination (MAE 4.3%): the same as that when using the accelerometer alone. Moreover, our results show that the Kruskal-Wallis test can be used to discard up to 50% of the features, and yet improve the performance of classification algorithms. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Institute of Electrical and Electronics Engineers en
dc.relation.ispartof IEEE Sensors 2017
dc.relation.uri http://ieee-sensors2017.org/
dc.rights © 2017, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. en
dc.subject Human activity recognition en
dc.subject HAR en
dc.subject Sensors en
dc.title Sensor and feature selection for an emergency first responders activity recognition system en
dc.type Conference item en
dc.internal.authorcontactother Sebastian Scheurer, Insight Centre for Data Analytics, University College Cork, Cork, Ireland. T: +353-21-490-3000 E: sebastian.scheurer@insight-centre.org en
dc.internal.availability Full text available en
dc.description.version Accepted Version en
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder European Regional Development Fund en
dc.contributor.funder Enterprise Ireland en
dc.contributor.funder European Commission en
dc.description.status Peer reviewed en
dc.identifier.journaltitle IEEE Sensors 2017 en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/ en
dc.relation.project info:eu-repo/grantAgreement/EC/FP7::SP1::SP1-JTI/621272/EU/Sensor technologies enhanced safety and security of buildings and its occupants/SAFESENS en


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