Human activity recognition for emergency first responders via body-worn inertial sensors
| dc.contributor.author | Scheurer, Sebastian | |
| dc.contributor.author | Tedesco, Salvatore | |
| dc.contributor.author | Brown, Kenneth N. | |
| dc.contributor.author | O'Flynn, Brendan | |
| 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.date.accessioned | 2018-02-28T10:01:26Z | |
| dc.date.available | 2018-02-28T10:01:26Z | |
| dc.date.issued | 2017-06-01 | |
| dc.date.updated | 2018-02-15T09:56:49Z | |
| 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.status | Peer reviewed | en |
| dc.description.uri | https://bsn.embs.org/2017/ | en |
| dc.description.version | Accepted Version | en |
| dc.format.mimetype | application/pdf | en |
| 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.doi | 10.1109/BSN.2017.7935994 | |
| dc.identifier.endpage | 8 | en |
| dc.identifier.startpage | 5 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/5559 | |
| 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.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.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 |
