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Menelaos Bakopoulos, Sofia Tsekeridou, Eri Giannaka, Zheng-Hua Tan, & Ramjee Prasad. (2011). Command & control: Information merging, selective visualization and decision support for emergency handling. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Emergency situations call for the timely collaboration and error free communication of first responder (FR) teams from their Command Posts (CP) and between themselves. First responder teams must form and adapt their plans and actions as a real-time critical situation unfolds. This paper presents an advanced Command Post application that manages a diversity of FR teams during an emergency. Data from biometric, fire and/or gas sensors in addition to received annotated videos from first responders on site, carrying personal digital assistants (PDAs), are simultaneously managed. The presented system provides properly configured access to and alert-dependent visualization of real time location, biometric, gas, fire and annotated video data from FRs in the field to allow for effective reaction and decision support from CP personnel. Additionally, the system forms an information management system for all necessary information to be quickly handy during emergency handling, such as FR information, critical infrastructure information, historical information, etc. This system has been validated through qualitative analysis in a field trial at the M30 tunnel in Madrid by participating end users.
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Zeno Franco, Katinka Hooyer, Tanvir Roushan, Casey O'Brien, Nadiyah Johnson, Bill Watson, et al. (2018). Detecting & Visualizing Crisis Events in Human Systems: an mHealth Approach with High Risk Veterans. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 874–885). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Designing mHealth applications for mental health interventions has largely focused on education and patient self-management. Next generation applications must take on more complex tasks, including sensor-based detection of crisis events, search for individualized early warning signs, and support for crisis intervention. This project examines approaches to integrating multiple worn sensors to detect mental health crisis events in US military veterans. Our work has highlighted several practical and theoretical problems with applying technology to evaluation crises in human system, which are often subtle and difficult to detect, as compared to technological or natural crisis events. Humans often do not recognize when they are in crisis and under-report crises to prevent reputational damage. The current project explores preliminary use of the E4 Empatica wristband to characterize acute aggression using a combination of veteran self-report data on anger, professional actors simulating aggressive events, and preliminary efforts to discriminate between crisis data and early warning sign data.
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