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A distributed fuzzy system for dangerous events real-time alerting

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Abstract

In recent years, several dangerous events (such as terrorism, violent crimes, explosions) happened across the globe. In some cases, some events simultaneously occurred in different places in the same city. The primary method used for alerting authorities in these situations is the phone call of people that are near the location of the event. Commonly, in such case, people get excited phone calls that make complicated the work of the operator that needs to understand relevant information such as the location of the event, the number of individuals that are involved and so on. Moreover may also happen that scared or injured people are not able to communicate. But in these cases, rapid responses are fundamental to save human lives and to stop the criminal action. The pervasive use of powerful mobile devices embedding several kinds of sensors and providing great computational capabilities gives the technological support for developing more complex applications. In this paper, we propose a distributed fuzzy system that can infer in real-time critical situations by analysing data gathered from user’s smart-phones about the environment and the individual.

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Notes

  1. Data Source: American Heart Association, http://www.heart.org

  2. Center for Hearing and Communication, http://chchearing.org

References

  • Albashiti AI, Malkawi M, Khasawneh MA, Murad O (2018) A novel neuro-fuzzy model to detect human emotions using different set of vital factors with performance index measure. J Commun Softw Syst 14(1):121–129

    Google Scholar 

  • Avvenuti M, Cresci S, Marchetti A, Meletti C, Tesconi M (2014) Ears (earthquake alert and report system): a real time decision support system for earthquake crisis management. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 1749–1758

  • Bahir E, Peled A (2015) Real-time major events monitoring and alert system through social networks. J Conting Crisis Manag 23(4):210–220

    Article  Google Scholar 

  • Benincasa G, D’Aniello G, De Maio C, Loia V, Orciuoli F (2015) Towards perception-oriented situation awareness systems. In: Intelligent systems’ 2014, Springer, pp 813–824

  • Bonneau J, Anderson J, Danezis G (2009) Prying data out of a social network. In: Social network analysis and mining, 2009. ASONAM’09. International conference on advances in, IEEE, pp 249–254

  • Boonnithi S, Phongsuphap S (2011) Comparison of heart rate variability measures for mental stress detection. In: Computing in cardiology, 2011, IEEE, pp 85–88

  • Castillo C (2016) Big crisis data: social media in disasters and time-critical situations. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Cheng T, Wicks T (2014) Event detection using twitter: a spatio-temporal approach. PloS One 9(6):e97807

    Article  Google Scholar 

  • Corley CD, Dowling C, Rose SJ, McKenzie T (2013) Social sensor analytics: measuring phenomenology at scale. In: Intelligence and security informatics (ISI), 2013 IEEE international conference on, IEEE, pp 61–66

  • Council NR (2013) Public response to alerts and warnings using social media: report of a workshop on current knowledge and research gaps. National Academies, Washington, DC

    Google Scholar 

  • Dang TT, Truong H, Dang TK (2016) Automatic fall detection using smartphone acceleration sensor. IJACSA 7(12):123–129

    Google Scholar 

  • D’Aniello G, Gaeta M, Orciuoli F (2018) An approach based on semantic stream reasoning to support decision processes in smart cities. Telemat Inf 35(1):68–81

    Article  Google Scholar 

  • Foresti GL, Farinosi M, Vernier M (2015) Situational awareness in smart environments: socio-mobile and sensor data fusion for emergency response to disasters. J Ambient Intell Hum Comput 6(2):239–257

    Article  Google Scholar 

  • Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220

    Article  Google Scholar 

  • Hadley M, Sandoz P (2009) Jax-rs: Java\(^{{\rm TM}}\) api for restful web services. JSR 311:9–34

    Google Scholar 

  • Hwang S, Ryu M, Yang Y, Lee N (2012) Fall detection with three-axis accelerometer and magnetometer in a smartphone. In: Proceedings of the international conference on computer science and technology (CST 2012), Jeju, Korea

  • Imran M, Castillo C, Lucas J, Meier P, Vieweg S (2014) Aidr: Artificial intelligence for disaster response. In: Proceedings of the 23rd international conference on World Wide Web, ACM, pp 159–162

  • Jia N (2009) Detecting human falls with a 3-axis digital accelerometer. Analog Dialog 43(7):1–7

    Google Scholar 

  • Kreibig SD (2010) Autonomic nervous system activity in emotion: a review. Biol Psychol 84(3):394–421

    Article  Google Scholar 

  • Lahiri MK, Kannankeril PJ, Goldberger JJ (2008) Assessment of autonomic function in cardiovascular disease. J Am Coll Cardiol 51(18):1725–1733

    Article  Google Scholar 

  • Lee P, Lakshmanan LV, Milios EE (2013) Event evolution tracking from streaming social posts. arXiv preprint arXiv:13115978

  • Long LL, Srinivasan M (2013) Walking, running, and resting under time, distance, and average speed constraints: optimality of walk-run-rest mixtures. J R Soc Interface 10(81):20120980

    Article  Google Scholar 

  • Marcus A, Bernstein MS, Badar O, Karger DR, Madden S, Miller RC (2011) Twitinfo: aggregating and visualizing microblogs for event exploration. In: Proceedings of the SIGCHI conference on Human factors in computing systems, ACM, pp 227–236

  • Mathioudakis M, Koudas N (2010) Twittermonitor: trend detection over the twitter stream. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data, ACM, pp 1155–1158

  • Mawson AR (2005) Understanding mass panic and other collective responses to threat and disaster. Psychiatry Interpers Biol Process 68(2):95–113

    Article  Google Scholar 

  • Mayilvaganan M, Rajeswari K (2014) Risk factor analysis to patient based on fuzzy logic control system. Blood Press 60:40

    Google Scholar 

  • Meladianos P, Nikolentzos G, Rousseau F, Stavrakas Y, Vazirgiannis M (2015) Degeneracy-based real-time sub-event detection in twitter stream. In: ICWSM, pp 248–257

  • Miao G, Tatemura J, Hsiung WP, Sawires A, Moser LE (2009) Extracting data records from the web using tag path clustering. In: Proceeding of the 18th international conference on World wide web, ACM, pp 981–990

  • Nie D, Wang XW, Shi LC, Lu BL (2011) Eeg-based emotion recognition during watching movies. In: Neural engineering (NER), 2011 5th international IEEE/EMBS conference on, IEEE, pp 667–670

  • Pereira T, Almeida PR, Cunha JP, Aguiar A (2017) Heart rate variability metrics for fine-grained stress level assessment. Comput Methods Progr Biomed 148:71–80

    Article  Google Scholar 

  • Robinson B, Power R, Cameron M (2013) A sensitive twitter earthquake detector. In: Proceeding of the 22nd international conference on world wide web, ACM, pp 999–1002

  • Rotstein A, Inbar O, BerginskyT, Meckel Y (2005) Preferred transition speed between walking and running: effects of training status. Med Sci Sports Exerc 37(11):1864

    Article  Google Scholar 

  • Salfinger A, Schwinger W, Retschitzegger W, Pröll B (2016) Mining the disaster hotspots-situation-adaptive crowd knowledge extraction for crisis management. In: Cognitive methods in situation awareness and decision support (CogSIMA), 2016 IEEE international multi-disciplinary conference on, IEEE, pp 212–218

  • Sazonov ES, Klinkhachorn P, GangaRao HV, Halabe UB (2002) Fuzzy logic expert system for automated damage detection from changes in strain energy mode shapes. Nondestruct Test Eval 18(1):1–20

    Article  Google Scholar 

  • Schubert C, Lambertz M, Nelesen R, Bardwell W, Choi JB, Dimsdale J (2009) Effects of stress on heart rate complexity—a comparison between short-term and chronic stress. Biol Psychol 80(3):325–332

    Article  Google Scholar 

  • Seong H, Lee J, Shin T, Kim W, Yoon Y (2004) The analysis of mental stress using time-frequency distribution of heart rate variability signal. In: Engineering in medicine and biology society, 2004. IEMBS’04. 26th annual international conference of the IEEE, IEEE, vol 1, pp 283–285

  • Sutton J, Spiro ES, Johnson B, Fitzhugh S, Gibson B, Butts CT (2014) Warning tweets: serial transmission of messages during the warning phase of a disaster event. Inf Commun Soc 17(6):765–787

    Article  Google Scholar 

  • Vieweg S, Hughes AL, Starbird K, Palen L (2010) Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In: Proceeding of the SIGCHI conference on human factors in computing systems, ACM, pp 1079–1088

  • Yates D, Paquette S (2011) Emergency knowledge management and social media technologies: a case study of the 2010 haitian earthquake. Int J Inf Manag 31(1):6–13

    Article  Google Scholar 

  • Yin J, Lampert A, Cameron M, Robinson B, Power R (2012) Using social media to enhance emergency situation awareness. IEEE Intell Syst 27(6):52–59

    Article  Google Scholar 

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Correspondence to Patrizia Ribino.

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Ribino, P., Lodato, C. A distributed fuzzy system for dangerous events real-time alerting. J Ambient Intell Human Comput 10, 4263–4282 (2019). https://doi.org/10.1007/s12652-018-1102-y

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