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

  • Sebastian ScheurerEmail author
  • Salvatore Tedesco
  • Òscar Manzano
  • Kenneth N. Brown
  • Brendan O’Flynn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)

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.

Keywords

Human activity recognition Machine learning Boosting Inertial sensors 

Notes

Acknowledgements

This publication emanated from research supported by research grants from Science Foundation Ireland (SFI) and the European Development Fund under grant numbers SFI/12/RC/2289 and 13/RC/2077-CONNECT, and the European funded project SAFESENS under the ENIAC program in association with Enterprise Ireland (IR20140024).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Insight Centre for Data Analytics, Department of Computer ScienceUniversity College CorkCorkIreland
  2. 2.Tyndall National InstituteUniversity College CorkCorkIreland

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