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Situation Detection based on Activity Recognition in Disaster Scenarios
Patrick Lieser
author
Alaa Alhamoud
author
Hosam Nima
author
Björn Richerzhagen
author
Sanja Huhle
author
Doreen Böhnstedt
author
Ralf Steinmetz
author
2018
Rochester Institute of Technology
Rochester, NY (USA)
English
In disaster situations like earthquakes and hurricanes, people have difficulties accessing shelter and requesting help. Many smartphone applications provide behavioral advice or means to communicate during such situations. However, to what extent a person is affected by a disaster is often unclear, as these applications rely on the user's subjective assessment. Therefore, detecting a user's situation is key to provide more meaningful information in such applications and to allows first responders to better assess incoming messages. We propose a predictive model that recognizes four normal and ten disaster-related activities achieving an average f1-score of up to 90.1\%, solely based on sensor readings of the subject's mobile device. We conduct an extensive measurement-based evaluation to assess the impact of individual model parameters on the prediction accuracy. Our model is orientation-independent, position-independent, and subject-independent, making it an ideal foundation for future context-aware emergency applications.
Disaster Relief
Activity Recognition
Machine Learning
Wearables
exported from refbase (http://idl.iscram.org/show.php?record=2147), last updated on Mon, 25 Nov 2019 10:50:40 +0100
text
http://idl.iscram.org/files/patricklieser/2018/2147_PatrickLieser_etal2018.pdf
PatrickLieser_etal2018
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management
Iscram 2018
Kees Boersma
editor
Brian Tomaszeski
editor
ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management
2018
Rochester Institute of Technology
Rochester, NY (USA)
conference publication
737
753
978-0-692-12760-5
2411-3387
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