Monitoring emergency first responders' activities via gradient boosting and inertial sensor data

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Show simple item record Scheurer, Sebastian Tedesco, Salvatore Manzano, Óscar Brown, Kenneth N. O'Flynn, Brendan 2020-02-13T16:26:46Z 2020-02-13T16:26:46Z 2019-01-18
dc.identifier.citation Scheurer S., Tedesco S., Manzano Ò., Brown K.N., O’Flynn B. (2019) Monitoring Emergency First Responders’ Activities via Gradient Boosting and Inertial Sensor Data. In: Brefeld U. et al. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2018. Lecture Notes in Computer Science, vol 11053. Springer, Cham, pp. 691-694. doi: 10.1007/978-3-030-10997-4_53 en
dc.identifier.volume 11053 en
dc.identifier.startpage 691 en
dc.identifier.endpage 694 en
dc.identifier.doi 10.1007/978-3-030-10997-4_53 en
dc.description.abstract Emergency first response teams during operations expend much time to communicate their current location and status with their leader over noisy radio communication systems. We are developing a modular system to provide as much of that information as possible to team leaders. One component of the system is a human activity recognition (HAR) algorithm, which applies an ensemble of gradient boosted decision trees (GBT) to features extracted from inertial data captured by a wireless-enabled device, to infer what activity a first responder is engaged in. An easy-to-use smartphone application can be used to monitor up to four first responders' activities, visualise the current activity, and inspect the GBT output in more detail. en
dc.description.sponsorship Science Foundation Ireland (SFI) and European Commision (European Development Fund under grant numbers SFI/12/RC/2289 and 13/RC/2077-CONNECT, European funded project SAFESENS under the ENIAC program in association with Enterprise Ireland (IR20140024)) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Springer en
dc.rights © Springer Nature Switzerland AG 2019. This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: en
dc.subject Boosting en
dc.subject Human activity recognition en
dc.subject Inertial sensors en
dc.subject Machine learning en
dc.title Monitoring emergency first responders' activities via gradient boosting and inertial sensor data en
dc.type Conference item en
dc.internal.authorcontactother Brendan O'Flynn, Tyndall Microsystems, University College Cork, Cork, Ireland. +353-21-490-3000 Email: en
dc.internal.availability Full text available en 2020-02-13T16:12:23Z
dc.description.version Accepted Version en
dc.internal.rssid 493164357
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder European Regional Development Fund en
dc.contributor.funder Enterprise Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Lecture Notes in Computer Science en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Dublin, Ireland en
dc.internal.IRISemailaddress en
dc.internal.IRISemailaddress en
dc.internal.IRISemailaddress en
dc.internal.IRISemailaddress en
dc.internal.bibliocheck Check funding SFI 13/RC/2077-CONNECT 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/EC/FP7::SP1::SP1-JTI/621272/EU/Sensor technologies enhanced safety and security of buildings and its occupants/SAFESENS en

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