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


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

Publication Type: 
Refereed Conference Meeting Proceeding
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
ECML PKDD 2018: Machine Learning and Knowledge Discovery in Databases
Digital Object Identifer (DOI): 
Publication Date: 
Conference Location: 
National University of Ireland, Cork (UCC)
Tyndall National Institute
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