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

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dc.contributor.authorScheurer, Sebastian
dc.contributor.authorTedesco, Salvatore
dc.contributor.authorManzano, Óscar
dc.contributor.authorBrown, Kenneth N.
dc.contributor.authorO'Flynn, Brendan
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.contributor.funderEnterprise Irelanden
dc.date.accessioned2020-02-13T16:26:46Z
dc.date.available2020-02-13T16:26:46Z
dc.date.issued2019-01-18
dc.date.updated2020-02-13T16:12:23Z
dc.description.abstractEmergency 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.sponsorshipScience 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.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationScheurer 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_53en
dc.identifier.doi10.1007/978-3-030-10997-4_53en
dc.identifier.endpage694en
dc.identifier.journaltitleLecture Notes in Computer Scienceen
dc.identifier.startpage691en
dc.identifier.urihttps://hdl.handle.net/10468/9649
dc.identifier.volume11053en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.projectinfo:eu-repo/grantAgreement/EC/FP7::SP1::SP1-JTI/621272/EU/Sensor technologies enhanced safety and security of buildings and its occupants/SAFESENSen
dc.relation.urihttps://link.springer.com/chapter/10.1007/978-3-030-10997-4_53
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: http://dx.doi.org/10.1007/978-3-030-10997-4_53en
dc.subjectBoostingen
dc.subjectHuman activity recognitionen
dc.subjectInertial sensorsen
dc.subjectMachine learningen
dc.titleMonitoring emergency first responders' activities via gradient boosting and inertial sensor dataen
dc.typeConference itemen
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