Monitoring emergency first responders' activities via gradient boosting and inertial sensor data

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Scheurer, Sebastian
Tedesco, Salvatore
Manzano, Óscar
Brown, Kenneth N.
O'Flynn, Brendan
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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.
Boosting , Human activity recognition , Inertial sensors , Machine learning
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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