Towards a Crowd-Sensing Enhanced Situation Awareness System for Crisis Management

Author(s):  
Andrea Salfinger ◽  
Werner Retschitzegger ◽  
Wieland Schwinger ◽  
Birgit Pröll
Author(s):  
Andrea Salfinger ◽  
Sylva Girtelschmid ◽  
Birgit Proll ◽  
Werner Retschitzegger ◽  
Wieland Schwinger

Author(s):  
Akhila Manne ◽  
Madhu Bala Myneni

Social media has redefined crisis management in the recent years. Extraction of situation awareness information from social media sites such as Twitter, Facebook, Instagram, etc. is a non-trivial task once the required framework is established. Unfortunately, most public safety authorities are still suspicious of using social media in engaging and disseminating information. This chapter reports on how social media can be effectively used in the field of emergency management along with the opportunities and challenges put forth. The chapter starts with a discussion on the functions of social media and its trustworthiness. It provides a description of the framework for disaster management system and the methodology to be adopted. The methodology consists of volunteer classification, methods of data collection, challenges faced, event detection, and data characterization with currently available disaster management tools. The chapter concludes with the division between practice and research and moves toward envisioning how social media may be used as a resource in emergency management.


2020 ◽  
Vol 24 (2) ◽  
pp. 508-525
Author(s):  
Chuanrong Zhang ◽  
Tian Zhao ◽  
E. Lynn Usery ◽  
Dalia Varanka ◽  
Weidong Li

Author(s):  
Md Wasiur Rahman ◽  
Marina L. Gavrilova

Gait not only defines the way a person walks, but also provides insights on an individual's daily routine, mental state or even cognitive function. The importance of incorporating cognitive behavior and analysis in biometric systems has been noted recently. In this article, authors develop a biometric security system using gait-based skeletal information obtained from Microsoft Kinect v1 sensor. The gait cycle is calculated by detecting the three consecutive local minima between the joint distance of left and right ankles. Authors have utilized the distance feature vector for each of the joints with respect to other joints in the gait cycle. After mean and variance features are extracted from the distance feature vector, the KNN algorithm is used for classification purpose. The classification accuracy of the authors' approach is 93.33%. Experimental results show that the proposed approach achieves better recognition accuracy then other state-of-the-art approaches. Incorporating gait biometric in a situation awareness system for identification of a mental state is one of the future directions of this research.


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