Human activity recognition for emergency first responders via body-worn inertial sensors

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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 2018-02-28T10:01:26Z
dc.date.available 2018-02-28T10:01:26Z
dc.date.issued 2017-06-01
dc.identifier.citation Scheurer, S., Tedesco, S., Brown, K. N., O'Flynn, B. (2017) 'Human activity recognition for emergency first responders via body-worn inertial sensors', IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Eindhoven, Netherlands, 9-12 May. doi: 10.1109/BSN.2017.7935994 en
dc.identifier.startpage 5 en
dc.identifier.endpage 8 en
dc.identifier.uri http://hdl.handle.net/10468/5559
dc.identifier.doi 10.1109/BSN.2017.7935994
dc.description.abstract Every year over 75 000 firefighters are injured and 159 die in the line of duty. Some of these accidents could be averted if first response team leaders had better information about the situation on the ground. The SAFESENS project is developing a novel monitoring system for first responders designed to provide response team leaders with timely and reliable information about their firefighters' status during operations, based on data from wireless inertial measurement units. In this paper we investigate if Gradient Boosted Trees (GBT) could be used for recognising 17 activities, selected in consultation with first responders, from inertial data. By arranging these into more general groups we generate three additional classification problems which are used for comparing GBT with k-Nearest Neighbours (kNN) and Support Vector Machines (SVM). The results show that GBT outperforms both kNN and SVM for three of these four problems with a mean absolute error of less than 7%, which is distributed more evenly across the target activities than that from either kNN or SVM. en
dc.description.sponsorship Seventh Framework Programme (SAFESENS project under the ENIAC Program in association with Enterprise Ireland - IR20140024) en
dc.description.uri https://bsn.embs.org/2017/ en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Institute of Electrical and Electronics Engineers (IEEE) en
dc.relation.ispartof IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
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 Support vector machines en
dc.subject Legged locomotion en
dc.subject Sensors en
dc.subject Monitoring en
dc.subject Accelerometers en
dc.subject Machine learning algorithms en
dc.subject Feature extraction en
dc.title Human activity recognition for emergency first responders via body-worn inertial sensors en
dc.type Conference item en
dc.internal.authorcontactother Kenneth Brown, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: k.brown@cs.ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2018-02-15T09:56:49Z
dc.description.version Accepted Version en
dc.internal.rssid 425982288
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder European Regional Development Fund en
dc.contributor.funder Enterprise Ireland en
dc.contributor.funder Seventh Framework Programme en
dc.description.status Peer reviewed en
dc.internal.copyrightchecked Yes en
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Eindhoven, Netherlands en
dc.internal.IRISemailaddress k.brown@cs.ucc.ie 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


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