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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2018: Machine Learning and Knowledge Discovery in Databases pp 691–694Cite as

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

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Part of the Lecture Notes in Computer Science book series (LNAI,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

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  • DOI: 10.1007/978-3-030-10997-4_53
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References

  1. Brahmi, I.H., Abruzzo, G., Walsh, M., Sedjelmaci, H., O’Flynn, B.: A fuzzy logic approach for improving the tracking accuracy in indoor localisation applications. In: Wireless Days Conference. IEEE, April 2018

    Google Scholar 

  2. Karantonis, D.M., Narayanan, M.R., Mathie, M., Lovell, N.H., Celler, B.G.: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. Trans. Inf. Technol. Biomed. 10(1), 156–167 (2006)

    CrossRef  Google Scholar 

  3. Khodjaev, J., Tedesco, S., O’Flynn, B.: Improved NLOS error mitigation based on LTS algorithm. Prog. Electromagn. Res. Lett. 58, 133–139 (2016)

    CrossRef  Google Scholar 

  4. Medtronic: Zephyr™ performance systems for first responders and industrial safety (2017). https://www.zephyranywhere.com

  5. Scheurer, S., Tedesco, S., Brown, K.N., O’Flynn, B.: Human activity recognition for emergency first responders via body-worn inertial sensors. In: International Conference on Wearable and Implantable Body Sensor Networks. IEEE, May 2017

    Google Scholar 

  6. Scheurer, S., Tedesco, S., Brown, K.N., O’Flynn, B.: Sensor and feature selection for an emergency first responders activity recognition system. In: Sensors. IEEE, October 2017

    Google Scholar 

  7. Sreenivasan, R.R., Nirmalya, R.: Recent trends in machine learning for human activity recognition–a survey. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 8(4) (2018)

    Google Scholar 

  8. Tedesco, S., Khodjaev, J., O’Flynn, B.: A novel first responders location tracking system: architecture and functional requirements. In: Mediterranean Microwave Symposium. IEEE, November 2015

    Google Scholar 

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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).

Author information

Authors and Affiliations

  1. Insight Centre for Data Analytics, Department of Computer Science, University College Cork, Cork, Ireland

    Sebastian Scheurer & Kenneth N. Brown

  2. Tyndall National Institute, University College Cork, Cork, Ireland

    Salvatore Tedesco, Òscar Manzano & Brendan O’Flynn

Authors
  1. Sebastian Scheurer
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  2. Salvatore Tedesco
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  3. Òscar Manzano
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  4. Kenneth N. Brown
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  5. Brendan O’Flynn
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Corresponding author

Correspondence to Sebastian Scheurer .

Editor information

Editors and Affiliations

  1. Leuphana University, Lüneburg, Germany

    Prof. Dr. Ulf Brefeld

  2. National University of Ireland, Galway, Ireland

    Edward Curry

  3. IBM Research - Ireland, Dublin, Ireland

    Elizabeth Daly

  4. University College Dublin, Dublin, Ireland

    Dr. Brian MacNamee

  5. Nokia (Ireland), Dublin, Ireland

    Alice Marascu

  6. Vodafone, Milan, Italy

    Fabio Pinelli

  7. IBM Research - Ireland, Dublin, Ireland

    Michele Berlingerio

  8. University College Dublin, Dublin, Ireland

    Neil Hurley

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Scheurer, S., Tedesco, S., Manzano, Ò., Brown, K.N., O’Flynn, B. (2019). Monitoring Emergency First Responders’ Activities via Gradient Boosting and Inertial Sensor Data. In: , et al. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2018. Lecture Notes in Computer Science(), vol 11053. Springer, Cham. https://doi.org/10.1007/978-3-030-10997-4_53

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  • DOI: https://doi.org/10.1007/978-3-030-10997-4_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-10996-7

  • Online ISBN: 978-3-030-10997-4

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