Charlotte Hellgren, & Björn J.E. Johansson. (2012). Reducing workload by navigational support in dynamic situations. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: By presenting continuously updated heading and distance information on a small head-mounted display (HMD), as a supplement to a GPS-receiver, we examined if workload could be reduced and performance increased, when navigating in a demanding situation. The purpose was to present limited but sufficient information to facilitate navigation. The technique was tested on ground troops, but could also be used by rescue services and police in situations that require navigation in unknown environments. The main findings were that the workload was reduced in one aspect (during navigation) but increased in another (looking for foot placement). There were no clear differences in performance, except that participants stopped fewer times to look at the GPS-receiver if they had updated heading and distance information. This suggests that a supplement display with minimal information could be useful when navigating with a GPS-receiver in an unknown environment. © 2012 ISCRAM.
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Rosemarijn Looije, Mark A. Neerincx, & Geert-Jan M. Kruijff. (2007). Affective collaborative robots for safety & crisis management in the field. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 497–506). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The lack of human-robot collaboration currently presents a bottleneck to widespread use of robots in urban search & rescue (USAR) missions. The paper argues that an important aspect of realizing human-robot collaboration is collaborative control, and the recognition and expression of affect. Affective collaborative robots can enhance joint human-robot performance by adapting the robot's (social) role and interaction to the user's affective state and the context. Current USAR robots lack these capabilities. This paper presents theory, application domains, and requirements for affective collaborative robots based on the current state of the art. With methods from cognitive architectures, affective computing, and human-robot interaction, three core functions of affective collaborative robots can be realized: sliding autonomy, affective communication, and adaptive attitude. These robot functions can substantially enhance the efficiency and effectiveness of rescue workers and meanwhile reduce their cognitive workload. Furthermore, robots with such functions can approach civilians in the field appropriately.
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Mark Parent, Jean-François Gagnon, Tiago H. Falk, & Sébastien Tremblay. (2016). Modeling the Operator Functional State for Emergency Response Management. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: New technologies are available for emergency management experts to help them cope with challenges such as information overload, multitasking and fatigue. Among these technologies, a wide variety of physiological sensors can now be deployed to measure the Operator Functional State (OFS). To be truly useful, such measures should not only characterize the overall OFS, but also the specific dimensions such as stress or mental workload. This experiment aimed to (1) design a multi-dimensional model of OFS, and (2) test its application to an emergency management situation. First, physiological data of participants were collected during controlled experimental tasks. Then, a support vector classifier of mental workload and stress was trained. Finally, the resulting model was tested during an emergency management simulation. Results suggest that the model could be applied to emergency management situations, and leave the door open for its application to emergency response on the field.
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