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Author (up) Rahul Pandey; Brenda Bannan; Hemant Purohit pdf  isbn
  Title CitizenHelper-training: AI-infused System for Multimodal Analytics to assist Training Exercise Debriefs at Emergency Services Type Conference Article
  Year 2020 Publication ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 42-53  
  Keywords Training Exercise, Emergency Preparedness, AI system, Learning Analytics, Responder Training.  
  Abstract The adoption of Artificial Intelligence (AI) technologies across various real-world applications for human performance augmentation demonstrates an unprecedented opportunity for emergency management. However, the current exploration of AI technologies such as computer vision and natural language processing is highly focused on emergency response and less investigated for the preparedness and mitigation phases. The training exercises for emergency services are critical to preparing responders to perform effectively in the real-world, providing a venue to leverage AI technologies. In this paper, we demonstrate an application of AI to address the challenges in augmenting the performance of instructors or trainers in such training exercises in real-time, with the explicit aim of reducing cognitive overload in extracting relevant knowledge from the voluminous multimodal data including video recordings and IoT sensor streams. We present an AI-infused system design for multimodal stream analytics and lessons from its use during a regional training exercise for active violence events.  
  Address George Mason University; George Mason University; George Mason University  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-5 ISBN 2411-3391 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 2206  
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