Monitoring emergency first responders' activities via gradient boosting and inertial sensor data
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Accepted version
Date
2019-01-18
Authors
Scheurer, Sebastian
Tedesco, Salvatore
Manzano, Óscar
Brown, Kenneth N.
O'Flynn, Brendan
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Published Version
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.
Description
Keywords
Boosting , Human activity recognition , Inertial sensors , Machine learning
Citation
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: Brefeld U. et al. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2018. Lecture Notes in Computer Science, vol 11053. Springer, Cham, pp. 691-694. doi: 10.1007/978-3-030-10997-4_53
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© Springer Nature Switzerland AG 2019. This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-10997-4_53