Toward Improving Situation Awareness and Team Coordination in Emergency Response with Sensor and Video Data Streams

2019 ◽  
pp. 257-269
Author(s):  
Samantha Dubrow ◽  
Brenda Bannan
Keyword(s):  
Emergency Response ◽  
Situation Awareness ◽  
Data Streams ◽  
Video Data ◽  
Team Coordination

Coordinated Awareness of Situation by Teams (CAST): Measuring Team Situation Awareness of a Communication Glitch

2005 ◽  
Vol 49 (3) ◽  
pp. 274-277 ◽  
Author(s):  
Jamie C. Gorman ◽  
Nancy J. Cooke ◽  
Harry K. Pederson ◽  
O. Connor Olena ◽  
Janie A. DeJoode
Keyword(s):  
Situation Awareness ◽  
Communication Channel ◽  
Experimental Results ◽  
Team Coordination ◽  
Team Members ◽  
Knowledge Based ◽  
Unmanned Air Vehicle ◽  
Air Vehicle ◽  
Low Dimension ◽  
Team Situation Awareness

A coordination-based measure of team situation awareness is presented and contrasted with knowledge-based measurement. The measure is applied to team awareness of a communication channel failure (glitch) during a simulated unmanned air vehicle reconnaissance experiment. Experimental results are reported, including the findings that not all team members should be identically aware of the glitch and that appropriate levels of coordination are an important precursor of team situation awareness. The results are discussed in terms of the application of coordination metrics to support the understanding of team situation awareness. The use of team coordination as a low-dimension variable of team functionality is scalable over a variety of team sizes and expertise distributions.

Citizen-to-Citizen Resource Sharing in Emergency Response

Crowdsourcing ◽  
2019 ◽  
pp. 578-605
Author(s):  
Soon Ae Chun ◽  
Jaideep S. Vaidya ◽  
Vijayalakshmi Atluri ◽  
Basit Shafiq ◽  
Nabil R. Adam
Keyword(s):  
Citizen Participation ◽  
Emergency Response ◽  
Situation Awareness ◽  
Resource Sharing ◽  
Large Scale ◽  
Situational Awareness ◽  
Government Agencies ◽  
Mobile Crowdsourcing ◽  
Disaster Situation ◽  
The Government

During large-scale manmade or natural disasters, such as Superstorm Sandy and Hurricanes Harvey and Irma, collaborations among government agencies, NGOs, and businesses need to be coordinated to provide necessary resources to respond to emergency events. However, resources from citizens themselves are underutilized, such as their equipment or expertise. The citizen participation via social media enhanced the situational awareness, but the response management is still mainly handled by the government or government-sanctioned partners. By harnessing the power of citizen crowdsourcing, government agencies can create enhanced disaster situation awareness and facilitate effective utilization of resources provided by citizen volunteers, resulting in more effective disaster responses. This chapter presents a public engagement in emergency response (PEER) framework that provides an online and mobile crowdsourcing platform for incident reporting and citizens' resource volunteering as well as an intelligent recommender system to match-make citizen resources with emergency tasks.

Citizen-to-Citizen Resource Sharing in Emergency Response

2018 ◽  
pp. 130-164 ◽  
Author(s):  
Soon Ae Chun ◽  
Jaideep S. Vaidya ◽  
Vijayalakshmi Atluri ◽  
Basit Shafiq ◽  
Nabil R. Adam
Keyword(s):  
Citizen Participation ◽  
Emergency Response ◽  
Situation Awareness ◽  
Resource Sharing ◽  
Large Scale ◽  
Situational Awareness ◽  
Government Agencies ◽  
Mobile Crowdsourcing ◽  
Disaster Situation ◽  
The Government

During large-scale manmade or natural disasters, such as Superstorm Sandy and Hurricanes Harvey and Irma, collaborations among government agencies, NGOs, and businesses need to be coordinated to provide necessary resources to respond to emergency events. However, resources from citizens themselves are underutilized, such as their equipment or expertise. The citizen participation via social media enhanced the situational awareness, but the response management is still mainly handled by the government or government-sanctioned partners. By harnessing the power of citizen crowdsourcing, government agencies can create enhanced disaster situation awareness and facilitate effective utilization of resources provided by citizen volunteers, resulting in more effective disaster responses. This chapter presents a public engagement in emergency response (PEER) framework that provides an online and mobile crowdsourcing platform for incident reporting and citizens' resource volunteering as well as an intelligent recommender system to match-make citizen resources with emergency tasks.

Cognitively Motivated Novelty Detection in Video Data Streams

2007 ◽  
pp. 209-233
Author(s):  
James M. Kang ◽  
Muhammad Aurangzeb Ahmad ◽  
Ankur Teredesai ◽  
Roger Gaborski
Keyword(s):  
Data Streams ◽  
Novelty Detection ◽  
Video Data

scholarly journals DESIGN AND EXPERIMENT OF A HIGH PAYLOAD FIXED WING VTOL UAV SYSTEM FOR EMERGENCY RESPONSE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1715-1722
Author(s):  
H. Wu ◽  
Z. Wang ◽  
B. Ren ◽  
L. Wang ◽  
J. Zhang ◽  
...  
Keyword(s):  
Emergency Response ◽  
Inertial Navigation System ◽  
High Accuracy ◽  
Video Data ◽  
Multiple Sensors ◽  
Mosaic Image ◽  
Vtol Uav ◽  
Processing Software ◽  
Design And Practice ◽  
High Payload

Abstract. With the development of UAV technologies, the advantages of hybrid VTOL UAV have been realized and taken in emergency response. But, former hybrid VTOL UAV is lack of capacities on payload and endurance, which restrict the integration of multiple sensors. In this paper, a high payload fixed wing VTOL UAV, which has 20 kg payload and more than 3 hours endurance, is used to design a UAV system for emergency response. Multiple sensors including an optronics pod, PhaseOne IXM-100 camera, high accuracy inertial navigation system and three-axis stable head are integrated with it. Based on this, specific processing software is developed to process the video data and image which could meet the requirements of emergency response in different stages. Experiment results shown that the precision of mosaic image is about 10m and the precision of orthoimage is about 1m. This work could be reference for the design and practice of UAV system with multiple sensors.

scholarly journals Learning neural decoders without labels using multiple data streams

2021 ◽  
Author(s):  
Steven M. Peterson ◽  
Rajesh P. N. Rao ◽  
Bingni W. Brunton
Keyword(s):  
Real World ◽  
Data Streams ◽  
Video Data ◽  
Neural Decoding ◽  
Computer Interfaces ◽  
Multiple Data ◽  
Real World Applications ◽  
Recorded Data ◽  
Robust Adaptive ◽  
Decoding Accuracy

AbstractRecent advances in neural decoding have accelerated the development of brain-computer interfaces aimed at assisting users with everyday tasks such as speaking, walking, and manipulating objects. However, current approaches for training neural decoders commonly require large quantities of labeled data, which can be laborious or infeasible to obtain in real-world settings. One intriguing alternative uses self-supervised models that share self-generated pseudo-labels between two data streams; such models have shown exceptional performance on unlabeled audio and video data, but it remains unclear how well they extend to neural decoding. Here, we learn neural decoders without labels by leveraging multiple simultaneously recorded data streams, including neural, kinematic, and physiological signals. Specifically, we apply cross-modal, self-supervised deep clustering to decode movements from brain recordings; these decoders are compared to supervised and unimodal, self-supervised models. We find that sharing pseudo-labels between two data streams during training substantially increases decoding performance compared to unimodal, self-supervised models, with accuracies approaching those of supervised decoders trained on labeled data. Next, we develop decoders trained on three modalities that match or slightly exceed the performance of supervised models, achieving state-of-the-art neural decoding accuracy. Cross-modal decoding is a flexible, promising approach for robust, adaptive neural decoding in real-world applications without any labels.

RimSim Response Hospital Evacuation

2011 ◽  
Vol 3 (3) ◽  
pp. 1-15 ◽  
Author(s):  
Bruce Campbell ◽  
Chris Weaver
Keyword(s):  
Emergency Response ◽  
Situation Awareness ◽  
King County ◽  
Pilot Studies ◽  
Research Teams ◽  
Emergency Responders ◽  
Cognitive Aids ◽  
And Training

To aid emergency response teams in training and planning for potential community-wide emergency crises, two coordinated research teams centered in King County, Washington have developed software-based tools to provide cognitive aids for improved planning and training for emergency response scenarios. After reporting the results previously of using the tools in pilot studies of increasing complexity, the implementation teams have been searching out community-wide emergency response teams working on emergency response plans that might benefit from use of the tools. In this paper, the authors describe the tools, the application of them to a countywide hospital evacuation scenario, and the evaluation of their value to emergency responders for improving situation awareness and insight generation.

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