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.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11053 LNAI, pp. 691–694). Springer Verlag. https://doi.org/10.1007/978-3-030-10997-4_53
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