@inbook{d91e38e4eafd4b3c847079c8ed2d4250,
title = "Monitoring emergency first responders{\textquoteright} activities via gradient boosting and inertial sensor data",
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{\textquoteright} activities, visualise the current activity, and inspect the GBT output in more detail.",
keywords = "Boosting, Human activity recognition, Inertial sensors, Machine learning",
author = "Sebastian Scheurer and Salvatore Tedesco and {\`O}scar Manzano and Brown, \{Kenneth N.\} and Brendan O{\textquoteright}Flynn",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018 ; Conference date: 10-09-2018 Through 14-09-2018",
year = "2019",
doi = "10.1007/978-3-030-10997-4\_53",
language = "English",
isbn = "9783030109967",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "691--694",
editor = "Ulf Brefeld and Alice Marascu and Fabio Pinelli and Edward Curry and Brian MacNamee and Neil Hurley and Elizabeth Daly and Michele Berlingerio",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings",
address = "Germany",
}