M. Yuksel, W. Wang, S. Chaudhry, D. Turgut, and N. Kapucu

Challenges and Opportunities in Utilizing IoT-Based Stress Maps as a Community Mood Detector


Cite as:

M. Yuksel, W. Wang, S. Chaudhry, D. Turgut, and N. Kapucu. Challenges and Opportunities in Utilizing IoT-Based Stress Maps as a Community Mood Detector. In 2019 IEEE Symposium on Technologies for Homeland Security, pp. 1–7, November 2019.

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Abstract:

Stress has been known to cause physical and mental issues like depression, anxiety, insomnia, lower immunity, stroke, as well as leading to suicidal thoughts or violence towards others. Stress is not just a state of mind, but it is measurable. With the ubiquity of Internet of Things (IoT), and the integration with highly sensitive biosensors, it may be feasible to use these devices for detecting stress in public places. Moreover, correlating such stress data with social media streams can lead to insights into the psychological well-being of the community as a whole. We present a framework of such a community stress map based on social media and explore techniques for gathering data for measuring stress levels as well as detecting abnormal levels. This stress map can then be leveraged by emergency and crisis response teams for public safety and help them be proactive in allocating resources to the stressed areas indicated in the map.

BibTeX:

@inproceedings{Yuksel-2019-HST,
   author = "M. Yuksel and W. Wang and S. Chaudhry and D. Turgut and N. Kapucu",
   title = "Challenges and Opportunities in Utilizing IoT-Based Stress Maps as a Community Mood Detector",
   booktitle = "2019 IEEE Symposium on Technologies for Homeland Security",
   month = "November",
   pages = "1-7",
   year = "2019",
    abstract = {Stress has been known to cause physical and mental issues like depression, anxiety, insomnia, lower immunity, stroke, as well as leading to suicidal thoughts or violence towards others. Stress is not just a state of mind, but it is measurable. With the ubiquity of Internet of Things (IoT), and the integration with highly sensitive biosensors, it may be feasible to use these devices for detecting stress in public places. Moreover, correlating such stress data with social media streams can lead to insights into the psychological well-being of the community as a whole. We present a framework of such a community stress map based on social media and explore techniques for gathering data for measuring stress levels as well as detecting abnormal levels. This stress map can then be leveraged by emergency and crisis response teams for public safety and help them be proactive in allocating resources to the stressed areas indicated in the map. },
}

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