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Designing a Multimodal Analytics System to Improve Emergency Response Training

  • Hemant PurohitEmail author
  • Samantha Dubrow
  • Brenda Bannan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11590)

Abstract

The role of high-fidelity simulation-based training is critical for preparing first responders to perform effectively in emergency response and firefighting environments. However, training simulation instructors currently rely on direct observation and radio-based audio communication methods to observe behaviors and evaluate interactions of trainees to provide feedback to trainees during debriefing sessions. Such human-driven evaluative methods, while valuable, lose fidelity of information for learning due to working memory limitations and the mind’s inability to capture all relevant behavioral information in realtime and to provide relevant detailed information that is easily interpretable to instructors. Thus, there is a need and the potential to explore alternative data collection and processing methods leveraging advanced wireless technologies to attempt to enhance the effectiveness of simulation training process for emergency response, to ultimately help save lives and better prepare our communities building emergency and disaster resilience. In this paper, we present a multimodal streaming analytics system to support learning by leveraging a user-centered design approach in consultation with a regional fire and rescue training academy. This conceptualized system provides real-time collection, visualization, and analysis of heterogeneous data streams including location sensing using Internet of Things (IoT) devices, audio communication, video observations, as well as social media and 911 call log streams. We describe the associated design challenges and lessons learned from the initial prototyping activities to strive toward enhancing the situational awareness and learning of emergency response personnel and leadership instructors with iterative design cycles at the regional fire and rescue training academy.

Keywords

Learning Realtime streams IoT Smart cities Disaster resilience 

Notes

Acknowledgements

Authors would like to thank undergraduate research assistant Mohammad Rana for implementing the preliminary system and U.S. National Science Foundation grants DRL-1637263 and IIS-1815459 for partially supporting this research. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hemant Purohit
    • 1
    Email author
  • Samantha Dubrow
    • 2
  • Brenda Bannan
    • 3
  1. 1.Information Sciences and Technology, Volgenau School of EngineeringGeorge Mason UniversityFairfaxUSA
  2. 2.Department of Psychology, College of Humanities and Social SciencesGeorge Mason UniversityFairfaxUSA
  3. 3.Division of Learning Technologies, College of Education and Human DevelopmentGeorge Mason UniversityFairfaxUSA

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