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.
- Human activity recognition
- Machine learning
- Inertial sensors
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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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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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