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Dragos Datcu, & Leon J.M. Rothkrantz. (2007). The use of active appearance model for facial expression recognition in crisis environments. 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. 515–524). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In the past a crisis event was notified by local witnesses that use to make phone calls to the special services. They reported by speech according to their observation on the crisis site. The recent improvements in the area of human computer interfaces make possible the development of context-aware systems for crisis management that support people in escaping a crisis even before external help is available at site. Apart from collecting the people's reports on the crisis, these systems are assumed to automatically extract useful clues during typical human computer interaction sessions. The novelty of the current research resides in the attempt to involve computer vision techniques for performing an automatic evaluation of facial expressions during human-computer interaction sessions with a crisis management system. The current paper details an approach for an automatic facial expression recognition module that may be included in crisis-oriented applications. The algorithm uses Active Appearance Model for facial shape extraction and SVM classifier for Action Units detection and facial expression recognition.
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Siska Fitrianie, & Leon J.M. Rothkrantz. (2007). An automated crisis online dispatcher. 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. 525–536). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: An experimental automated dialogue system that plays the role of a crisis hotline dispatcher is currently developed. Besides controlling the communication flow, this system is able to retrieve information about crisis situations from user's input. It offers a natural user interaction by the ability to perceive and respond to human emotions. The system has an emotion recognizer that is able to recognize the emotional loading from user's linguistic content. The recognizer uses a database that contains selected keywords on a 2D “arousal” and “valence” scale. The output of the system provides not only the information about the user's emotional state but also an indication of the urgency of his/her information regarding to crisis. The dialogue system is able to start a user friendly dialogue, taking care of the content, context and emotional loading of user's utterances.
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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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Zhenke Yang, & Leon J.M. Rothkrantz. (2007). Emotion sensing for context sensitive interpretation of crisis reports. 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. 507–514). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The emotional qualities of a report play an important role in the evaluation of eye witness reports in crisis centers. Human operators in the crisis center can use the amount of anxiety and stress detected in a spoken report to rapidly estimate the possible impact and urgency of a report and the appropriate response to the reporter. This paper presents ongoing work in automated multi-modal emotion sensing of crisis reports in order to reduce the cognitive load on human operators. Our approach is based on the work procedures adopted by the crisis response center Rijnmond environmental agency (DCMR) and assumes a spoken dialogue between a reporter and a crisis control center. We use an emotion model based on conceptual graphs that is continually evaluated while the dialogue continues. We show how the model can be applied to interpret crisis report in a fictional toxic gas dispersion scenario.
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