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A Comprehensive Framework for Elderly Healthcare Monitoring in Smart Environment

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Technology for Smart Futures

Abstract

Global demographic trends clearly point out that the world population is aging due to a combination of dropping mortality rates and increasing life expectancy. The global community is seeking solutions to address the pressing societal challenge of providing effective and efficient healthcare to the elderly. It is difficult to achieve satisfactory results merely by relying on scaling up conventional healthcare infrastructures. These techniques will not be sufficient to independently assist the elderly to live alone in a house mainly if they are suffering from chronic diseases, thus require continuous health monitoring. It is imperative to exploit the advances in emerging technologies such as biosensors, mobile devices, and communication networks to provide remote health monitoring services along with the physical infrastructural facilities. Remote and continuous monitoring of patients with chronic diseases is being considered as an efficient and cost-effective solution, which will reduce the burden on the elderly and his/her families, as well as on the health government’s expenses. While considerable research and development is being undertaken in this field, most of the current state of the art reflects a lack of a concerted and cohesive approach to develop an integrated remote health monitoring system. This chapter surveys existing pervasive healthcare systems and classifies them as academia based or industrial based, and then it develops a set of criteria to compare these solutions. It discusses some drawbacks of existing solutions and proposes future directions in pervasive healthcare, which are predicted to shape future pervasive healthcare systems. Finally, it proposes a novel healthcare monitoring framework based on an integrated and scalable architecture, which provides flexibility and enables interoperability between myriads of healthcare monitoring devices. The proposed framework relies on the analytics of both evidenced data collected from sensors as well as the massive data collected from social networks. A prototype of the framework has been developed to evaluate the applicability and the efficiency of monitoring and analytics practices.

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References

  1. Al Ahmad M., Al-Hemairy, M., & Amin, A. inventor. (2016). A Novel Algorithm for fast disease detection based on vital signs. USA Patent Application no. US 62/377,223. Filed on 19 August 2016.

    Google Scholar 

  2. Al-Hemairy, M., Serhani, A., Atif Y., & Amin, S. (2013). Classification of Pervasive Healthcare Systems. In Developments in eSystems Engineering (DeSE), 6th International Conference on eSystems Engineering (pp. 105–110). Abu Dhabi.

    Google Scholar 

  3. Al-Hemairy, M., Serhani, M. A., Amin, S., Alahmad M., & Hijji M. (2016). Integrated and scalable architecture for providing cost-effective remote health monitoring, Developments in eSystems Engineering (DeSE). In 2016 9th International Conference on eSystems Engineering, Liverpool.

    Google Scholar 

  4. Anderson, S., & Wittwer, W. (2004). Using bar-code point-of-care technology for patient safety. Journal for Healthcare Quality, 26(6), 5–11. doi:10.1111/j.1945-1474.2004.tb00527.x.

    Article  Google Scholar 

  5. Anderson, K., Valenzuela, E., & Wittwer, G. (2011). Wine export shocks and wine tax reform in Australia: Regional consequences using an economy-wide approach*. Economic Papers: A journal of applied economics and policy, 30(3), 386–399. doi:10.1111/j.1759-3441.2011.00124.x.

    Article  Google Scholar 

  6. Andre, D., & Teller, A. (2005). Health. Care. Anywhere. Today. Studies in Health Technology and Informatics, 118, 89–110.

    Google Scholar 

  7. Anliker, U., Ward, J. A., Lukowicz, P., Troster, G., Dolveck, F., Baer, M., Keita, F., Schenker, E. B., Catarsi, F., Coluccini, L., Belardinelli, A., Shklarski, D., Alon, M., Hirt, E., Schmid, R., & Vuskovic, M. (2004). AMON: A wearable Multiparameter medical monitoring and alert system. IEEE Transactions on Information Technology in Biomedicine, 8(4), 415–427. doi:10.1109/titb.2004.837888.

    Article  Google Scholar 

  8. Banerjee, S., Steenkeste, F., Couturier, P., Debray, M., & Franco, A. (2003). Telesurveillance of elderly patients by use of passive infra-red sensors in a ‘smart’ room. Journal of Telemedicine and Telecare, 9(1), 23–29. doi:10.1258/135763303321159657.

    Article  Google Scholar 

  9. Bardram, J. E. (2005). Activity-based computing: Support for mobility and collaboration in ubiquitous computing. Personal and Ubiquitous Computing, 9(5), 312–322. doi:10.1007/s00779-004-0335-2.

    Article  Google Scholar 

  10. Bardram, J., & Christensen, H. (2007). Pervasive computing support for hospitals: An overview of the activity-based computing project. IEEE Pervasive Computing, 6(1), 44–51. doi:10.1109/mprv.2007.19.

    Article  Google Scholar 

  11. Benharref, A., & Serhani, M. A. (2014). Novel cloud and SOA-based framework for e-health monitoring using wireless biosensors. IEEE Journal of Biomedical and Health Informatics, 18(1), 46–55. doi:10.1109/jbhi.2013.2262659.

    Article  Google Scholar 

  12. Benharref, A., Serhani, M. A., & Nujum, A. R. (2014, August). Closing the loop from continuous M-health monitoring to fuzzy logic-based optimized recommendations. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 2698–2701). IEEE.

    Google Scholar 

  13. Blobel, B. (2007). Comparing approaches for advanced e-health security infrastructures. International Journal of Medical Informatics, 76(5–6), 454–459. doi:10.1016/j.ijmedinf.2006.09.012.

    Article  Google Scholar 

  14. Bohn, J., Coroamă, V., Langheinrich, M., Mattern, F., & Rohs, M. (2004). Living in a world of smart everyday objects – Social, economic, and ethical implications. Human and Ecological Risk Assessment: An International Journal, 10(5), 763–785. doi:10.1080/10807030490513793.

    Article  Google Scholar 

  15. Borthakur, D. (2007). Hadoop. In Hadoop, Retrieved 4 September 2013 from http://hadoop.apache.org .

  16. Bottazzi, D., Corradi, A., & Montanari, R. (2006). Context-aware middleware solutions for anytime and anywhere emergency assistance to elderly people. IEEE Communications Magazine, 44(4), 82–90. doi:10.1109/mcom.2006.1632653.

    Article  Google Scholar 

  17. Camarinha-Matos, L. M., & Afsarmanesh, H. (2004). TeleCARE: Collaborative virtual elderly care support communities. The Journal on Information Technology in Healthcare, 2(2), 73–86.

    Google Scholar 

  18. Cattamanchi, A., Smith, R., Steingart, K. R., Metcalfe, J. Z., Date, A., Coleman, C., Marston, B. J., Huang, L., Hopewell, P. C., & Pai, M. (2011). Interferon-gamma release assays for the diagnosis of latent tuberculosis infection in HIV-infected individuals: A systematic review and meta-analysis. Journal of Acquired Immune Deficiency Syndromes, 56(3), 230–238. doi:10.1097/qai.0b013e31820b07ab.

    Article  Google Scholar 

  19. Chalmers, C., Hurst, W., Mackay, M., & Fergus, P. (2015, October). Smart health monitoring using the advance metering infrastructure. In Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference on (pp. 2297–2302). IEEE.

    Google Scholar 

  20. Chen, C. I., Liu, C. Y., Li, Y. C., Chao, C. C., Liu, C. T., Chen, C. F., & Kuan, C. F. (2005). Pervasive observation medicine: The application of RFID to improve patient safety in observation unit of hospital emergency department. Studies in Health Technology and Informatics, 116, 311–315.

    Google Scholar 

  21. Cisco context-aware healthcare solution (n.d..) Available at: http://www.21.com/web/strategy/healthcare/Context-Aware_for_Healthcare.html. Accessed 15 November 2016.

  22. Coronato, A., & Pietro, G. D. (2010). Formal specification of wireless and pervasive healthcare applications. ACM Transactions on Embedded Computing Systems, 10(1), 1–18. doi:10.1145/1814539.1814551.

    Article  Google Scholar 

  23. Cruz-Correia, R. J., Vieira-Marques, P. M., Ferreira, A. M., Almeida, F. C., Wyatt, J. C., & Costa-Pereira, A. M. (2007). Reviewing the integration of patient data: How systems are evolving in practice to meet patient needs. BMC Medical Informatics and Decision Making, 7(1). doi:10.1186/1472-6947-7-14.

  24. De Jager, D., Wood, A. L., Merrett, G. V., Al-Hashimi, B. M., O’Hara, K., Shadbolt, N. R., & Hall, W. (2011, May). A low-power, distributed, pervasive healthcare system for supporting memory. In Proceedings of the First ACM MobiHoc Workshop on Pervasive Wireless Healthcare (p. 5). ACM.

    Google Scholar 

  25. Dey, A. K. (2001). Understanding and using context. Personal and Ubiquitous Computing, 5(1), 4–7. doi:10.1007/s007790170019.

    Article  Google Scholar 

  26. Ducatel, K., Bogdanowicz, M., Scapolo, F., Leijten, J., & Burgelman, J. C. (2001). Scenarios for ambient intelligence in 2010. Office for official publications of the European Communities.

    Google Scholar 

  27. Edlich (2009). NoSQL. In NoSQL. Retrieved 1 September 2013 from http://nosql-database.org.

  28. Hansen, T. R., Bardram, J. E., & Soegaard, M. (2006). Moving out of the lab: Deploying pervasive technologies in a hospital. IEEE Pervasive Computing, 5(3), 24–31. doi:10.1109/mprv.2006.53.

    Article  Google Scholar 

  29. Hayes, A. F. (2014). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: Guilford Publications.

    Google Scholar 

  30. Hijji, M., Amin, S., Iqbal, R., & Harrop, W. (2013, December). A Critical Evaluation of the Rational Need for an IT Management System for Flash Flood Events in Jeddah, Saudi Arabia. In Developments in eSystems Engineering (DeSE), 2013 Sixth International Conference on (pp. 209–214). IEEE.

    Google Scholar 

  31. Hijji, M., Amin, S., Iqbal, R., & Harrop, W. (2015). The significance of using “expert system” to assess the preparedness of training capabilities against different flash flood scenarios. Lecture Notes on Software Engineering, 3(3), 214–219. doi:10.7763/lnse.2015.v3.193.

    Article  Google Scholar 

  32. Katz, J., & Rice, R. (2009). Public views of mobile medical devices and services: A US national survey of consumer sentiments towards RFID healthcare technology. International Journal of Medical Informatics, 78(2), 104–114. doi:10.1016/j.ijmedinf.2008.06.001.

    Article  Google Scholar 

  33. Kenny, L. (2006). Exploring the business and social impacts of pervasive computing. Zurich: Swiss Re.

    Google Scholar 

  34. Lankton, N. K., & Wilson, E. V. (2007). Factors influencing expectations of e-health services within a direct-effects model of user satisfaction. e-Service Journal, 5(2), 85–112. doi:10.2979/esj.2007.5.2.85.

    Article  Google Scholar 

  35. Lee, T., & Mihailidis, A. (2005). An intelligent emergency response system: Preliminary development and testing of automated fall detection. Journal of Telemedicine and Telecare, 11(4), 194–198. doi:10.1258/1357633054068946.

    Article  Google Scholar 

  36. Lukowicz, P., Kirstein, T., & Troster, G. (2004). Wearable systems for health care applications. Methods of Information in Medicine-Methodik der Information in der Medizin, 43(3), 232–238.

    Google Scholar 

  37. Magedanz, T., Karmouch, A., Pierre, S., & Venieris, I. (2005). Mobility aware technologies and applications: Second international workshop, MATA 2005, Montreal, Canada, October 17–19, 2005, Proceedings (Vol. Vol. 3744). Berlin/Heidelberg: Springer.

    Book  Google Scholar 

  38. Mahoney, D. F., & Tarlow, B. (2006). Workplace response to virtual caregiver support and remote home monitoring of elders: The WIN project. Studies in Health Technology and Informatics, 122, 676.

    Google Scholar 

  39. Mattila, E., Korhonen, I., & Saranummi, N. (2007). Mobile and personal health and wellness management systems, In Pervasive computing in healthcare (pp. 105–134). Boca Raton: CRC Press.

    Google Scholar 

  40. Bardram, J., Mihailidis, A., & Wan, D. (2007). Pervasive computing in healthcare. Boca Raton: CRC Press.

    Google Scholar 

  41. Morris, M. E. (2005). Social networks as health feedback displays. IEEE Internet Computing, 9(5), 29–37. doi:10.1109/mic.2005.109.

    Article  Google Scholar 

  42. Munnelly, J., & Clarke, S. (2007, March). ALPH: A domain-specific language for crosscutting pervasive healthcare concerns. In Proceedings of the 2nd workshop on Domain specific aspect languages (p. 4). ACM.

    Google Scholar 

  43. Muras, J.A., Cahill, V., & Stokes E. K. (2006). In 2006 pervasive health Conference and workshops “a taxonomy of pervasive healthcare systems”.

    Google Scholar 

  44. Nachman, L., Baxi, A., Bhattacharya, S., Darera, V., Deshpande, P., Kodalapura, N., Mageshkumar, V., Rath, S., Shahabdeen, J., & Acharya, R. (2010, May). Jog falls: A pervasive healthcare platform for diabetes management. In International Conference on Pervasive Computing (pp. 94–111). Springer Berlin Heidelberg.

    Google Scholar 

  45. Office of technology policy an overview of e-waste policy issues (2006). Available at: http://www.bvsde.paho.org/bvsacd/cd57/recycling/intro.pdf. Accessed 15 November 2016.

  46. Olguin, D. O., Gloor, P. A., & Pentland, A. (2009, April). Wearable sensors for pervasive healthcare management. In 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare (pp. 1–4). IEEE.

    Google Scholar 

  47. Pai, F.-Y., & Huang, K.-I. (2011). Applying the technology acceptance model to the introduction of healthcare information systems. Technological Forecasting and Social Change, 78(4), 650–660. doi:10.1016/j.techfore.2010.11.007.

    Article  Google Scholar 

  48. Philipose, M., Fishkin, K. P., Perkowitz, M., Patterson, D. J., Fox, D., Kautz, H., & Hahnel, D. (2004). Inferring activities from interactions with objects. IEEE Pervasive Computing, 3(4), 50–57. doi:10.1109/mprv.2004.7.

    Article  Google Scholar 

  49. Rafalimanana, H., & Lai, M. (2013). World population ageing 2013. New York: United Nations, Department of Economic and Social Affairs. Population Division.

    Google Scholar 

  50. Riva, G. (2005). Ambient intelligence: The evolution of technology, communication and cognition towards the future of human-computer interaction (Vol. Vol. 6). Amsterdam: IOS Press.

    Google Scholar 

  51. Saha, D., & Mukherjee, A. (2003). Pervasive computing: A paradigm for the 21st century. Computer, 36(3), 25–31. doi:10.1109/mc.2003.1185214.

    Article  Google Scholar 

  52. Satyanarayanan, M. (2001). Pervasive computing: Vision and challenges. IEEE Personal Communications, 8(4), 10–17. doi:10.1109/98.943998.

    Article  Google Scholar 

  53. Schaechinger, U., Rockelein, W., Perk, A., Asbach, P., & Nerlich, M. (2003). NOAH – A mobile emergency care system. Studies in Health Technolgy and Informatics, 97, 147–158.

    Google Scholar 

  54. Scheffler, M., & Hirt, E. (2005). Wearable devices for telemedicine applications. Journal of Telemedicine and Telecare, 11(5), 11–14. doi:10.1258/1357633054461994.

    Article  Google Scholar 

  55. Schrooyen, F., Baert, I., Truijen, S., Pieters, L., Denis, T., Williame, K., & Weyn, M. (2006). Real time location system over WiFi in a healthcare environment. Journal on Information Technology in Healthcare, 4(6), 401–416.

    Google Scholar 

  56. Sixsmith, A., & Johnson, N. (2004). A smart sensor to detect the falls of the elderly. IEEE Pervasive Computing, 3(2), 42–47. doi:10.1109/mprv.2004.1316817.

    Article  Google Scholar 

  57. Thuemmler, C., Buchanan, W., Fekri, A. H., & Lawson, A. (2009). Radio frequency identification (RFID) in pervasive healthcare. International Journal of Healthcare Technology and Management, 10(1/2), 119. doi:10.1504/ijhtm.2009.023731.

    Article  Google Scholar 

  58. Tu, Y.-J., Zhou, W., & Piramuthu, S. (2009). Identifying RFID-embedded objects in pervasive healthcare applications. Decision Support Systems, 46(2), 586–593. doi:10.1016/j.dss.2008.10.001.

    Article  Google Scholar 

  59. Varshney, U. (2007). Pervasive healthcare and wireless health monitoring. Mobile Networks and Applications, 12(2–3), 113–127. doi:10.1007/s11036-007-0017-1.

    Article  Google Scholar 

  60. Varshney, U. (2009). Pervasive healthcare computing: EMR/EHR, wireless and health monitoring. Boston: Springer Science & Business Media.

    Book  Google Scholar 

  61. Washburn, S. C., & Hornberger, C. A. (2008). Nurse educator guidelines for the management of heart failure. The Journal of Continuing Education in Nursing, 39(6), 263–267. doi:10.3928/00220124-20080601-10.

    Article  Google Scholar 

  62. World Population Ageing Report (2013) Available at: http://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2013.pdf. Accessed 1 October 2016.

  63. Ziefle, M., & Rocker, C. (2010). Acceptance of pervasive healthcare systems: A comparison of different implementation concepts, pervasive computing technologies for healthcare (PervasiveHealth), 2010. In 4th International Conference on No Permissions.

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering and Computing, Coventry University, Coventry, UK

    M. Al Hemairy & S. Amin

  2. College of Information Technology, UAE University, Al Ain, Abu Dhabi, UAE

    M. Serhani

  3. College of Engineering, UAE University, Al Ain, Abu Dhabi, UAE

    M. Alahmad

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  1. M. Al Hemairy
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  2. M. Serhani
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  3. S. Amin
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  4. M. Alahmad
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Correspondence to M. Al Hemairy .

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Editors and Affiliations

  1. University of Suffolk, Suffolk, United Kingdom

    Mohammad Dastbaz

  2. Department of Computer Science, The University of Georgia, Athens, Georgia, USA

    Hamid Arabnia

  3. Sheffield Hallam University, Sheffield, United Kingdom

    Babak Akhgar

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Al Hemairy, M., Serhani, M., Amin, S., Alahmad, M. (2018). A Comprehensive Framework for Elderly Healthcare Monitoring in Smart Environment. In: Dastbaz, M., Arabnia, H., Akhgar, B. (eds) Technology for Smart Futures. Springer, Cham. https://doi.org/10.1007/978-3-319-60137-3_6

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  • DOI: https://doi.org/10.1007/978-3-319-60137-3_6

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