What did you do today?: discovering daily routines from large-scale mobile data
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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)
Location and Navigation Support for Emergency Responders: A Survey
IEEE Pervasive Computing
EmotionSense: a mobile phones based adaptive platform for experimental social psychology research
Proceedings of the 12th ACM international conference on Ubiquitous computing
Passive and In-Situ assessment of mental and physical well-being using mobile sensors
Proceedings of the 13th international conference on Ubiquitous computing
Social fMRI: Investigating and shaping social mechanisms in the real world
Pervasive and Mobile Computing
Robust Voice Activity Detection Using Long-Term Signal Variability
IEEE Transactions on Audio, Speech, and Language Processing
StressSense: detecting stress in unconstrained acoustic environments using smartphones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Automatically characterizing places with opportunistic crowdsensing using smartphones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Human interaction discovery in smartphone proximity networks
Personal and Ubiquitous Computing
Mining large-scale smartphone data for personality studies
Personal and Ubiquitous Computing
Sensing group proximity dynamics of firefighting teams using smartphones
Proceedings of the 2013 International Symposium on Wearable Computers
Evaluating daily life activity using smartphones as novel outcome measure for surgical pain therapy
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
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Firefighting is a dangerous task and many research projects have aimed at supporting firefighters during missions by developing new and often costly equipment. In contrast to previous approaches, we use the smartphone to monitor firefighters during real-world missions in order to provide objective data that can be used in post-incident briefings and trainings. In this paper, we present CoenoFire, a smartphone based sensing system aimed at monitoring temporal and behavioral performance indicators of firefighting missions. We validate the performance metrics showing that they can indicate why certain teams performed faster than others in a training scenario conducted by 16 firefighting teams. Furthermore, we deployed CoenoFire over a period of six weeks in a professional fire brigade. In total, 71 firefighters participated in our study and the collected data includes 76 real-world missions totaling to over 148 hours of mission data. Additionally, we visualize real-world mission data and show how mission feedback is supported by the data.