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I2VSM Approach: Self-monitoring of Patients Exploring Situational Awareness in IoT

  • Rogério AlbandesEmail author
  • Roger Machado
  • Jorge Barbosa
  • Adenauer Yamin
Conference paper
  • 14 Downloads
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 574)

Abstract

Mobility has become a daily practice of physicians, so it is possible that they remain periods of time without contact with the teams that support them in the treatment of patients. Longer periods between communications can cause delays in performing procedures, drug prescribing, etc. Considering this scenario, this work has as objective the conception an approach, called I2VSM, exploring IoT features and integrating: (i) a platform for acquisition of vital signs, (ii) an environment for contextual processing, which through customizable rules builds the Situational Awareness of the patients; and (iii) a textual and graphic display interface for these signals. As a source of vital signs, the MIMIC-III database is being used, which has been widely accepted by the international community for this purpose. In turn, for the evaluation of I2VSM together with health professionals, we explored the Technology Acceptance Model (TAM), obtaining promising results.

Keywords

Internet of Things Situational Awareness Vital signs 

References

  1. 1.
    Ahmed, M.U.: An intelligent healthcare service to monitor vital signs in daily life - a case study on health-IoT. Int. J. Eng. Res. Appl. (IJERA) 7(3), 43–55 (2017)Google Scholar
  2. 2.
    Austen, C., Patterson, C., Poots, A., Green, S., Weldring, T., Bell, D.: Using a local early warning scoring system as a model for the introduction of a national system. Acute Med. 11(2), 66–73 (2012)Google Scholar
  3. 3.
    Bleyer, A.J., et al.: Longitudinal analysis of one million vital signs in patients in an academic medical center. Resuscitation 82(11), 1387–1392 (2011)CrossRefGoogle Scholar
  4. 4.
    Brunker, C., Harris, R.: How accurate is the AVPU scale in detecting neurological impairment when used by general ward nurses? An evaluation study using simulation and a questionnaire. Intensive Crit. Care Nurs. 31(2), 69–75 (2015)CrossRefGoogle Scholar
  5. 5.
    Audit Commission, et al.: Critical to Success: The Place of Efficient and Effective Critical Care Services Within the Acute Hospital. Audit Commission, London (1999)Google Scholar
  6. 6.
    Costa Dias, E.: Condições de trabalho e saúde dos médicos: uma questão negligenciada e um desafio para a associação nacional de medicina do trabalho. Rev. bras. med. trab 13(2), 60–68 (2015)Google Scholar
  7. 7.
    Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35(8), 982–1003 (1989)CrossRefGoogle Scholar
  8. 8.
    Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5(1), 4–7 (2001).  https://doi.org/10.1007/s007790170019CrossRefGoogle Scholar
  9. 9.
    Dridi, A., Sassi, S., Faiz, S.: A smart IoT platform for personalized healthcare monitoring using semantic technologies. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1198–1203. IEEE (2017)Google Scholar
  10. 10.
    Endsley, M.: Designing for Situation Awareness: An Approach to User-Centered Design, 2nd edn. CRC Press, Boca Raton (2016). https://books.google.com.br/books?id=eRPBkapAsggCCrossRefGoogle Scholar
  11. 11.
    Evesti, A., Kanstrén, T., Frantti, T.: Cybersecurity situational awareness taxonomy. In: 2017 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (Cyber SA), pp. 1–8, June 2017.  https://doi.org/10.1109/CyberSA.2017.8073386
  12. 12.
    Jaiswal, K., Sobhanayak, S., Turuk, A.K., Bibhudatta, S.L., Mohanta, B.K., Jena, D.: An IoT-cloud based smart healthcare monitoring system using container based virtual environment in edge device. In: 2018 International Conference on Emerging Trends and Innovations in Engineering and Technological Research (ICETIETR), pp. 1–7. IEEE (2018)Google Scholar
  13. 13.
    Jevon, P.: How to ensure patient observations lead to prompt identification of tachypnoea. Nurs. Times 106(2), 12–14 (2010)Google Scholar
  14. 14.
    Johnson, A.E., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016)CrossRefGoogle Scholar
  15. 15.
    Karthikeyan, S., Devi, K.V., Valarmathi, K.: Internet of Things: hospice appliances monitoring and control system. In: 2015 Online International Conference on Green Engineering and Technologies (IC-GET), pp. 1–6. IEEE (2015)Google Scholar
  16. 16.
    Kelly, C.A., Upex, A., Bateman, D.N.: Comparison of consciousness level assessment in the poisoned patient using the alert/verbal/painful/unresponsive scale and the glasgow coma scale. Ann. Emerg. Med. 44(2), 108–113 (2004)CrossRefGoogle Scholar
  17. 17.
    Kruse, R.L., Ewigman, B.G., Tremblay, G.C.: The zipper: a method for using personal identifiers to link data while preserving confidentiality. Child Abuse Neglect 25(9), 1241–1248 (2001)CrossRefGoogle Scholar
  18. 18.
    Likert, R.: A Technique for the Measurement of Attitudes. Archives of Psychology (1932)Google Scholar
  19. 19.
    Lopes, J.L., et al.: A middleware architecture for dynamic adaptation in ubiquitous computing. J. Univ. Comput. Sci. 20(9), 1327–1351 (2014)Google Scholar
  20. 20.
    Maia, P., Baffa, A., Cavalcante, E., Delicato, F.C., Batista, T., Pires, P.F.: Uma plataforma de middleware para integração de dispositivos e desenvolvimento de aplicações em e-health. In: Anais do XXXIII SBRC, pp. 361–374 (2015)Google Scholar
  21. 21.
    Millikan, G.A.: The oximeter, an instrument for measuring continuously the oxygen saturation of arterial blood in man. Rev. Sci. Instrum. 13(10), 434–444 (1942)CrossRefGoogle Scholar
  22. 22.
    Obrist, P.A., Black, A., Brener, J., DiCara, L.V.: Cardiovascular Psychophysiology: Current Issues in Response Mechanisms, Biofeedback and Methodology. Routledge, Abingdon (2017)Google Scholar
  23. 23.
    Oey, C., Moh, S.: A survey on temperature-aware routing protocols in wireless body sensor networks. Sensors 13(8), 9860–9877 (2013)CrossRefGoogle Scholar
  24. 24.
    Onwubiko, C.: Situational Awareness in Computer Network Defense: Principles, Methods and Applications. IGI Global, Hershey (2012)CrossRefGoogle Scholar
  25. 25.
    Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the Internet of Things: a survey. Commun. Surv. Tutor. 16(1), 414–454 (2014).  https://doi.org/10.1109/SURV.2013.042313.00197CrossRefGoogle Scholar
  26. 26.
    Perry, A., Potter, P.: Clinical Nursing Skills & Techniques. Mosby (2002). https://books.google.com.br/books?id=OAZtAAAAMAAJ
  27. 27.
    Pires, P.F., Cavalcante, E., Barros, T., Delicato, F.C., Batista, T., Costa, B.: A platform for integrating physical devices in the Internet of Things. In: Proceedings of the 12th IEEE International Conference on Embedded and Ubiquitous Computing, pp. 234–241 (2014)Google Scholar
  28. 28.
    Plate, J.D., Peelen, L.M., Leenen, L.P., Hietbrink, F.: Validation of the VitalPAC early warning score at the intermediate care unit. World J. Crit. Care Med. 7(3), 39 (2018)CrossRefGoogle Scholar
  29. 29.
    Pushover (2019). https://pushover.net/. Accessed 15 Feb 2019
  30. 30.
    Rufino, G.P., Gurgel, M.G., Pontes, T.D.C., Freire, E.: Avaliação de fatores determinantes do tempo de internação em clínica médica. Revista Brasileira Clínica Médica 10(4), 291–297 (2012)Google Scholar
  31. 31.
    Sezer, O.B., Dogdu, E., Ozbayoglu, A.M.: Context-aware computing, learning, and big data in Internet of Things: a survey. IEEE Internet Things J. 5(1), 1–27 (2018)CrossRefGoogle Scholar
  32. 32.
    Shaffer, F., Ginsberg, J.: An overview of heart rate variability metrics and norms. Front. Public Health 5, 258 (2017)CrossRefGoogle Scholar
  33. 33.
    Souza, R., Lopes, J., Geyer, C., Cardozo, A., Yamin, A., Barbosa, J.: An architecture for IoT management targeted to context awareness of ubiquitous applications. J. Univ. Comput. Sci. 24(10), 1452–1471 (2018)Google Scholar
  34. 34.
    Stedman, T.L.: Stedman’s Medical Dictionary for the Health Professions and Nursing. Lippincott Williams & Wilkins, Baltimore (2005)Google Scholar
  35. 35.
    Subbe, C., Kruger, M., Rutherford, P., Gemmel, L.: Validation of a modified early warning score in medical admissions. QJM 94(10), 521–526 (2001)CrossRefGoogle Scholar
  36. 36.
    Telegram (2019). https://telegram.org/. Accessed 15 Feb 2019
  37. 37.
    Turnbull, J.: The Docker Book: Containerization is the New Virtualization (2014)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2020

Authors and Affiliations

  1. 1.Catholic University of PelotasPelotasBrazil
  2. 2.Federal University of PelotasPelotasBrazil
  3. 3.Universidade do Vale do Rio dos SinosSão LeopoldoBrazil

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