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Smart and Pervasive Health Systems—Challenges, Trends, and Future Directions

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 69))

Abstract

Patients with chronic illnesses account for three-quarters of health care costs in the United States. For many patients, chronic conditions such as diabetes, coronary heart disease, and neurological disorders significantly affect their health-related quality of life (HQoL). Recent advances in wearable and ambient sensors and mobile health (mHealth) technologies have enabled remote monitoring of patient health and their symptoms thereby enhancing effective self-management of chronic conditions and improvement in patient HQoL. This paper presents a comprehensive review of recent trends and challenges in designing smart and pervasive health systems for monitoring patients with chronic illnesses. Recent work in developing wearable sensors and mHealth technologies for managing a variety of chronic diseases such as coronary heart disease, hypertension, and Parkinson’s disease is described in detail. The article addresses various research gaps in security, energy optimization, scalability, and interoperability. The paper concludes with future research directions and recommendations in user centric design, closed loop systems, value based treatment, signal processing, and device level research for efficient design and adoption of smart and pervasive health systems.

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References

  1. Piette, J.D., List, J., Rana, G.K., Townsend, W., Striplin, D., Heisler, M.: Mobile health devices as tools for worldwide cardiovascular risk reduction and disease management. Circulation 132(21), 2012–2027 (2015)

    Article  Google Scholar 

  2. Van Uem, J., Isaacs, T., Lewin, A., Bresolin, E., Salkovic, D., Espay, A., Maetzler, W.: A viewpoint on wearable technology-enabled measurement of wellbeing and health-related quality of life in Parkinson’s disease. J. Parkinson’s Dis. 6, 1–9 (2016)

    Google Scholar 

  3. Ryan, D., Price, D., Musgrave, S.D., Malhotra, S., Lee, A.J., Ayansina, D., Pinnock, H.: Clinical and cost effectiveness of mobile phone supported self monitoring of asthma: multicenter randomised controlled trial. BMJ 344, e1756 (2012)

    Article  Google Scholar 

  4. Seto, E., Leonard, K.J., Cafazzo, A.J., Barnsley, J., Masino, C., Ross, H.J.: Mobile phone-based telemonitoring for heart failure management: a randomized controlled trial. J. Med. Internet Res. 14(1), e31 (2012)

    Article  Google Scholar 

  5. Logan, A.G.: Transforming hypertension management using mobile health technology for telemonitoring and self-care support. Can. J. Cardiol. 29(5), 579–585 (2013)

    Article  MathSciNet  Google Scholar 

  6. Årsand, E., Frøisland, D.H., Skrøvseth, S.O., Chomutare, T., Tatara, N., Hartvigsen, G., Tufano, J.T.: Mobile health applications to assist patients with diabetes: lessons learned and design implications. J. Diabetes Sci. Technol. 6(5), 1197–1206 (2012)

    Article  Google Scholar 

  7. Bächlin, M., Plotnik, M., Roggen, D., Giladi, N., Hausdorff, J.M., Tröster, G.: A wearable system to assist walking of Parkinson’s disease patients. Methods Inf. Med. 49(1), 88–95 (2010)

    Google Scholar 

  8. Rigas, G., Tzallas, A.T., Tsipouras, M.G., Bougia, P., Tripoliti, E.E., Baga, D., Konitsiotis, S.: Assessment of tremor activity in the Parkinson’s disease using a set of wearable sensors. IEEE Trans. Inf. Technol. Biomed. 16(3), 478–487 (2012)

    Article  Google Scholar 

  9. Patel, S., Lorincz, K., Hughes, R., Huggins, N., Growdon, J., Standaert, D., Bonato, P.: Monitoring motor fluctuations in patients with Parkinson’s disease using wearable sensors. IEEE Trans. Inf. Technol. Biomed. 13(6), 864–873 (2009)

    Article  Google Scholar 

  10. Bachlin, M., Plotnik, M., Roggen, D., Maidan, I., Hausdorff, J.M., Giladi, N., Troster, G.: Wearable assistant for Parkinson’s disease patients with the freezing of gait symptom. IEEE Trans. Inf. Technol. Biomed. 14(2), 436–446 (2010)

    Article  Google Scholar 

  11. Storch, A., Schneider, C.B., Klingelhöfer, L., Odin, P., Fuchs, G., Jost, W.H., Ebersbach, G.: Quantitative assessment of non-motor fluctuations in Parkinson’s disease using the non-motor symptoms scale (NMSS). J. Neural Transm. 122(12), 1673–1684 (2015)

    Article  Google Scholar 

  12. Espay, A.J., Bonato, P., Nahab, F.B., Maetzler, W., Dean, J.M., Klucken, J., Reilmann, R.: Technology in Parkinson’s disease: challenges and opportunities. Mov. Disord. 31(9), 1272–1282 (2016)

    Article  Google Scholar 

  13. Farahani, B., Firouzi, F., Chang, V., Badaroglu, M., Constant, N., Mankodiya, K.: Towards fog-driven IoT eHealth: promises and challenges of IoT in medicine and healthcare. Future Gener. Comput. Syst. 78, 659–676 (2018)

    Article  Google Scholar 

  14. Stergiou, C., Psannis, K.E., Kim, B.G., Gupta, B.: Secure integration of IoT and cloud computing. Future Gener. Comput. Syst. 78, 964–975 (2018)

    Article  Google Scholar 

  15. Williamson, J., Liu, Q., Lu, F., Mohrman, W., Li, K., Dick, R., Shang, L.: Data sensing and analysis: challenges for wearables. In: The 20th Asia and South Pacific Design Automation Conference, pp. 136–141 (2015)

    Google Scholar 

  16. Baig, M.M., GholamHosseini, H., Moqeem, A.A., Mirza, F., Lindén, M.: A systematic review of wearable patient monitoring systems–current challenges and opportunities for clinical adoption. J. Med. Syst. 41(7), 115 (2017)

    Article  Google Scholar 

  17. Marcos, C., González-Ferrer, A., Peleg, M., Cavero, C.: Solving the interoperability challenge of a distributed complex patient guidance system: a data integrator based on HL7’s virtual medical record standard. J. Am. Med. Inf. Assoc. 22(3), 587–599 (2015)

    Article  Google Scholar 

  18. Li, Z., Wei, Z., Yue, Y., Wang, H., Jia, W., Burke, L.E., Sun, M.: An adaptive hidden Markov model for activity recognition based on a wearable multi-sensor device. J. Med. Syst. 39(5), 1–10 (2015)

    Article  Google Scholar 

  19. Wu, W., Zhang, H., Pirbhulal, S., Mukhopadhyay, S.C., Zhang, Y.T.: Assessment of biofeedback training for emotion management through wearable textile physiological monitoring system. IEEE Sens. J. 15(12), 7087–7095 (2015)

    Article  Google Scholar 

  20. Ledger, D., McCaffrey, D.: Inside wearables: How the science of human behavior change offers the secret to long-term engagement. Endeav. Partn. 200(93), 1–17 (2014)

    Google Scholar 

  21. Doty, T.J., Kellihan, B., Jung, T.P., Zao, J.K., Litvan, I.: The wearable multimodal monitoring system: a platform to study falls and near-falls in the real-world. In: International Conference on Human Aspects of IT for the Aged Population, pp. 412–422. Springer International Publishing (2015)

    Google Scholar 

  22. Wang, K., Redmond, S.J., Lovell, N.H.: Monitoring for elderly care: the role of wearable sensors in fall detection and fall prediction research. In: Eren, H., Webster J.G. (eds.) Telemedicine and Electronic Medicine, pp. 619–652. CRC Press (2015)

    Google Scholar 

  23. McCurdie, T., Taneva, S., Casselman, M., Yeung, M., McDaniel, C., Ho, W., Cafazzo, J.: mHealth consumer apps: the case for user-centered design. Biomed. Instr. Technol. 46(s2), 49–56 (2012)

    Article  Google Scholar 

  24. O’Sullivan, M., Blake, C., Cunningham, C., Boyle, G., Finucane, C.: Correlation of accelerometry with clinical balance tests in older fallers and non-fallers. Age Ageing 38(3), 308–313 (2009)

    Article  Google Scholar 

  25. Greene, B.R., O’Donovan, A., Romero-Ortuno, R., Cogan, L., Scanaill, C.N., Kenny, R.A.: Quantitative falls risk assessment using the timed up and go test. IEEE Trans. Biomed. Eng. 57(12), 2918–2926 (2010)

    Article  Google Scholar 

  26. Mynatt, E., Hager, G.D., Kumar, S., Lin, M., Patel, S., Stankovic, J., Wright, H.: Research opportunities and visions for smart and pervasive health. arXiv preprint arXiv:1706.09372 (2017)

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Correspondence to Ramesh Rajagopalan .

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Rajagopalan, R. (2020). Smart and Pervasive Health Systems—Challenges, Trends, and Future Directions. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-030-12388-8_29

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