Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Continuous Clustering of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
A Wireless Sensor Network and Incident Command Interface for Urban Firefighting
MOBIQUITOUS '07 Proceedings of the 2007 Fourth Annual International Conference on Mobile and Ubiquitous Systems: Networking&Services (MobiQuitous)
An empirical study of low-power wireless
ACM Transactions on Sensor Networks (TOSN)
Location and Navigation Support for Emergency Responders: A Survey
IEEE Pervasive Computing
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
Human interaction discovery in smartphone proximity networks
Personal and Ubiquitous Computing
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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Firefighters work in dangerous and unfamiliar situations under a high degree of time pressure and thus team work is of utmost importance. Relying on trained automatisms, firefighters coordinate their actions implicitly by observing the actions of their team members. To support training instructors with objective mission data, we aim to automatically detect when a firefighter is in-sight with other firefighters and to visualize the proximity dynamics of firefighting missions. In our approach, we equip firefighters with smartphones and use the built-in ANT protocol, a low-power communication radio, to measure proximity to other firefighters. In a second step, we cluster the proximity data to detect moving sub-groups. To evaluate our method, we recorded proximity data of 16 professional firefighting teams performing a real-life training scenario. We manually labeled six training sessions, involving 51 firefighters, to obtain 79 minutes of ground truth data. On average, our algorithm assigns each group member to the correct ground truth cluster with 80% accuracy. Considering height information derived from atmospheric pressure signals increases group assignment accuracy to 95%.