Sensor and feature selection for an emergency first responders activity recognition system
| 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 | European Commission | en |
| dc.date.accessioned | 2018-01-31T12:30:26Z | |
| dc.date.available | 2018-01-31T12:30:26Z | |
| dc.date.issued | 2017 | |
| 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.description.status | Peer reviewed | en |
| dc.description.version | Accepted Version | en |
| dc.format.mimetype | application/pdf | en |
| 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.doi | 10.1109/ICSENS.2017.8234090 | |
| dc.identifier.isbn | 978-1-5090-1012-7 | |
| dc.identifier.journaltitle | IEEE Sensors 2017 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/5355 | |
| dc.language.iso | en | en |
| dc.publisher | Institute of Electrical and Electronics Engineers | en |
| dc.relation.ispartof | IEEE Sensors 2017 | |
| 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 |
| 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 |
