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Monitoring Emergency First Responders' Activities via Gradient Boosting and Inertial Sensor Data

Authors: 

Sebastian Scheurer, Salvatore Tedesco, Òscar Manzano, Ken Brown, Brendan O'Flynn

Publication Type: 
Refereed Conference Meeting Proceeding
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.
Conference Name: 
Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Proceedings: 
ECML PKDD 2018: Machine Learning and Knowledge Discovery in Databases
Digital Object Identifer (DOI): 
10.1007/978-3-030-10997-4_53
Publication Date: 
18/01/2019
Conference Location: 
Ireland
Institution: 
National University of Ireland, Cork (UCC)
Tyndall National Institute
Open access repository: 
No