Skip to main content

Key Factors for Innovative Developments on Health Sensor-Based System

  • Conference paper
  • First Online:
Bioinformatics and Biomedical Engineering (IWBBIO 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10209))

Included in the following conference series:

Abstract

In the current technological revolution, the proliferation of sensors in smart devices and environments convert users into a real-life data source that ranges from the monitoring of vital signs to the recognition of their lifestyle, behavior and health. In this work, we describe current trends and issues on innovative healthcare systems, which are integrating wearable devices and smart environments into numerous health applications. The report includes a revision of the literature with academic, technical and legal concerns on the development of health solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abelson, P.H.: A third technological revolution. Science 279(5359), 2019–2109 (1998)

    Article  Google Scholar 

  2. Bower, J.L., Christensen, C.M.: Disruptive technologies: catching the wave, pp. 506–520 (1995). Harvard Business Review Video

    Google Scholar 

  3. Schwamm, L.H.: Telehealth: seven strategies to successfully implement disruptive technology and transform health care. Health Aff. 33(2), 200–206 (2014)

    Article  Google Scholar 

  4. Franz, N.K., Cox, R.A.: Time for disruptive innovation. J. Extension 50(2), 2COM1 (2012)

    Google Scholar 

  5. Christensen, C.M., Horn, M.B., Johnson, C.W.: Disrupting Class: How Disruptive Innovation will Change the Way the World Learns, vol. 98. McGraw-Hill, New York (2008)

    Google Scholar 

  6. Christensen, C.: The Innovatorś Dilemma: When New Technologies Cause Great Firms to Fail. Harvard Business Review Press, Boston (2013)

    Google Scholar 

  7. Assink, M.: Inhibitors of disruptive innovation capability: a conceptual model. Eur. J. Innov. Manag. 9(2), 215–233 (2006)

    Article  Google Scholar 

  8. Weiser, M.: The computer for the 21st century. Sci. Am. 265(3), 94–104 (1991)

    Article  Google Scholar 

  9. Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing. IEEE Commun. Mag. 48(9), 140–150 (2010)

    Article  Google Scholar 

  10. Malvey, D., Slovensky, D.J.: mHealth: Transforming Healthcare. Springer, New York (2014)

    Book  Google Scholar 

  11. Free, C., Phillips, G., Watson, L., Galli, L., Felix, L., Edwards, P., Haines, A.: The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis. PLoS Med. 10(1), e1001363 (2013)

    Article  Google Scholar 

  12. Wantland, D.J., Portillo, C.J., Holzemer, W.L., Slaughter, R., McGhee, E.M.: The effectiveness of Web-based vs. non-Web-based interventions: a meta-analysis of behavioral change outcomes. J. Med. Internet Res. 6(4), e40 (2004)

    Article  Google Scholar 

  13. Lymberis, A., Dittmar, A.: Advanced wearable health systems and applications-research and development efforts in the European Union. IEEE Eng. Med. Biol. Mag. 26(3), 29–33 (2007)

    Article  Google Scholar 

  14. Pantelopoulos, A., Bourbakis, N.G.: A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. Syst. Man Cybern. Appl. Rev. 40(1), 1–12 (2010)

    Article  Google Scholar 

  15. Patel, M.S., Asch, D.A., Volpp, K.G.: Wearable devices as facilitators, not drivers, of health behavior change. JAMA 313(5), 459–460 (2015)

    Article  Google Scholar 

  16. Szydlo, T., Konieczny, M.: Mobile and wearable devices in an open and universal system for remote patient monitoring. Microprocess. Microsyst. 46, 44–54 (2016)

    Article  Google Scholar 

  17. Albaghli, R., Anderson, K.M.: A vision for heart rate health through wearables. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive, Ubiquitous Computing: Adjunct, pp. 1101–1105. ACM, September 2016

    Google Scholar 

  18. MedTech Europe: The European Medical Technology Industry in Figures. MedTech Europe, Brussels (2013)

    Google Scholar 

  19. Garcia Lopez, P., Montresor, A., Epema, D., Datta, A., Higashino, T., Iamnitchi, A., Barcellos, M., Felber, P., Riviere, E.: Edge-centric computing: vision and challenges. ACM SIGCOMM Comput. Commun. Rev. 45(5), 37–42 (2015)

    Article  Google Scholar 

  20. Chen, L.W., Ho, Y.F., Kuo, W.T., Tsai, M.F.: Intelligent file transfer for smart handheld devices based on mobile cloud computing. Int. J. Commun. Syst. 30(1) (2015)

    Google Scholar 

  21. Xu, H., Collinge, W.O., Schaefer, L.A., Landis, A.E., Bilec, M.M., Jones, A.K.: Towards a commodity solution for the Internet of Things. Comput. Electr. Eng. 52, 138–156 (2016)

    Article  Google Scholar 

  22. Kopetz, H.: Internet of Things. In: Real-time systems, pp. 307–323. Springer, New York (2011)

    Google Scholar 

  23. Atzori, L., Iera, A., Morabito, G.: The Internet of Things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)

    Google Scholar 

  24. Kortuem, G., Kawsar, F., Sundramoorthy, V., Fitton, D.: Smart objects as building blocks for the Internet of Things. IEEE Internet Comput. 14(1), 44–51 (2010)

    Google Scholar 

  25. Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutorials 15(3), 1192–1209 (2013)

    Google Scholar 

  26. Chang, C.-Y., Lange, B., Zhang, M., Koenig, S., Requejo, P., Somboon, N., Sawchuk, A.A., Rizzo, A.A.: Towards pervasive physical rehabilitation using Microsoft Kinect. In: 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, pp. 159–162. IEEE, May 2012

    Google Scholar 

  27. Satyanarayanan, M.: Pervasive computing: vision and challenges. IEEE Pers. Commun. 8(4), 10–17 (2001)

    Article  Google Scholar 

  28. Varshney, U.: Pervasive healthcare and wireless health monitoring. Mob. Netw. Appl. 12(2–3), 113–127 (2007)

    Article  Google Scholar 

  29. Custodio, V., Herrera, F.J., Lpez, G., Moreno, J.I.: A review on architectures and communications technologies for wearable health-monitoring systems. Sensors 12(10), 13907–13946 (2012)

    Article  Google Scholar 

  30. Haefner, K.: Evolution of Information Processing Systems: An Interdisciplinary Approach for a New Understanding of Nature and Society. Springer Publishing Company Incorporated, Heidelberg (2011)

    Google Scholar 

  31. Emmanouilidis, C., Koutsiamanis, R.A., Tasidou, A.: Mobile guides: taxonomy of architectures, context awareness, technologies and applications. J. Netw. Comput. Appl. 36(1), 103–125 (2013)

    Article  Google Scholar 

  32. Makris, P., Skoutas, D.N., Skianis, C.: A survey on context-aware mobile and wireless networking: on networking and computing environments integration. IEEE Commun. Surv. Tutorials 15(1), 362–386 (2013)

    Article  Google Scholar 

  33. Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the Internet of Things: a survey. IEEE Commun. Surv. Tutorials 16(1), 414–454 (2014)

    Article  Google Scholar 

  34. Dicianno, B.E., Parmanto, B., Fairman, A.D., Crytzer, T.M., Daihua, X.Y., Pramana, G., Coughenour, D., Petrazzi, A.A.: Perspectives on the evolution of mobile (mHealth) technologies and application to rehabilitation. Phys. Ther. 95(3), 397–405 (2015)

    Article  Google Scholar 

  35. Silva, B.M., Rodrigues, J.J., de la Torre Díez, I., López-Coronado, M., Saleem, K.: Mobile-health: a review of current state in 2015. J. Biomed. Inform. 56, 265–272 (2015)

    Article  Google Scholar 

  36. Beratarrechea, A., Lee, A.G., Willner, J.M., Jahangir, E., Ciapponi, A., Rubinstein, A.: The impact of mobile health interventions on chronic disease outcomes in developing countries: a systematic review. Telemed. e-Health 20(1), 75–82 (2014)

    Article  Google Scholar 

  37. Martínez-Pérez, B., De La Torre-Díez, I., López-Coronado, M.: Mobile health applications for the most prevalent conditions by the World Health Organization: review and analysis. J. Med. Internet Res. 15(6), e120 (2013)

    Article  Google Scholar 

  38. Castelnuovo, G., Manzoni, G.M., Pietrabissa, G., Corti, S., Giusti, E.M., Molinari, E., Simpson, S.: Obesity and outpatient rehabilitation using mobile technologies: the potential mHealth approach. Front. Psychol. 5, 559 (2014)

    Article  Google Scholar 

  39. Friess, P.: Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems. River Publishers, Denmark (2013)

    Google Scholar 

  40. Zelkha, E., Epstein, B., Birrell, S., Dodsworth, C.: From devices to ambient intelligence. In: Digital Living Room Conference, vol. 6, June 1998

    Google Scholar 

  41. Marie, P., Desprats, T., Chabridon, S., Sibilla, M.: Extending ambient intelligence to the Internet of Things: new challenges for QoC management. In: Hervás, R., Lee, S., Nugent, C., Bravo, J. (eds.) UCAmI 2014. LNCS, vol. 8867, pp. 224–231. Springer, Cham (2014). doi:10.1007/978-3-319-13102-3_37

    Google Scholar 

  42. United Nations, Department of Economic and Social Affairs, Population Division: World Population Ageing (2013). ST/ESA/SER.A/348

    Google Scholar 

  43. Branger, J., Pang, Z.: From automated home to sustainable, healthy and manufacturing home: a new story enabled by the Internet-of-Things and Industry 4.0. J. Manag. Anal. 2(4), 314–332 (2014)

    Google Scholar 

  44. Yin, J., Tian, G., Feng, Z., Li, J.: Human activity recognition based on multiple order temporal information. Comput. Electr. Eng. 40(5), 1538–1551 (2014)

    Article  Google Scholar 

  45. Alam, M.M., Hamida, E.B.: Surveying wearable human assistive technology for life and safety critical applications: standards, challenges and opportunities. Sensors 14(5), 9153–9209 (2014)

    Article  Google Scholar 

  46. Van Hoof, J., Wouters, E.J.M., Marston, H.R., Vanrumste, B., Overdiep, R.A.: Ambient assisted living and care in The Netherlands: the voice of the user. In: Pervasive and Ubiquitous Technology Innovations for Ambient Intelligence, Environments, vol. 205 (2012)

    Google Scholar 

  47. Bellavista, P., Corradi, A., Fanelli, M., Foschini, L.: A survey of context data distribution for mobile ubiquitous systems. ACM Comput. Surv. (CSUR) 44(4), 24 (2012)

    Article  Google Scholar 

  48. Pei, Z., Deng, Z., Yang, B., Cheng, X.: Application-oriented wireless sensor network communication protocols, hardware platforms: a survey. In: IEEE International Conference on Industrial Technology, ICIT, pp. 1–6. IEEE (2008)

    Google Scholar 

  49. Villalonga, C., Razzaq, M.A., Khan, W.A., Pomares, H., Rojas, I., Lee, S., Banos, O.: Ontology-based high-level context inference for human behavior identification. Sensors 16(10), 1617 (2016)

    Article  Google Scholar 

  50. Nugent, C.D., Finlay, D.D., Davies, R.J., Wang, H.Y., Zheng, H., Hallberg, J., Synnes, K., Mulvenna, M.D.: homeML – an open standard for the exchange of data within smart environments. In: Okadome, T., Yamazaki, T., Makhtari, M. (eds.) ICOST 2007. LNCS, vol. 4541, pp. 121–129. Springer, Heidelberg (2007). doi:10.1007/978-3-540-73035-4_13

    Chapter  Google Scholar 

  51. Weichhart, G., Molina, A., Chen, D., Whitman, L.E., Vernadat, F.: Challenges and current developments for sensing, smart and sustainable enterprise systems. Comput. Ind. 79, 34–46 (2015)

    Article  Google Scholar 

  52. Balan, R.K., Satyanarayanan, M., Park, S.Y., Okoshi, T.: Tactics-based remote execution for mobile computing. In: Proceedings of the 1st International Conference on Mobile Systems, Applications and Services, pp. 273–286. ACM, May 2003

    Google Scholar 

  53. Verissimo, P., Rodrigues, L.: Distributed Systems for System Architects, vol. 1. Springer Science & Business Media, New York (2012)

    MATH  Google Scholar 

  54. Liu, G.: Distributing network services and resources in a mobile communications network. U.S. Patent No. 5,825,759. Washington, DC: U.S. Patent and Trademark Office (1998)

    Google Scholar 

  55. Chesbrough, H.: Open Business Models: How to Thrive in the New Innovation Landscape. Harvard Business Press, Cambridge (2013)

    Google Scholar 

  56. Henning, M.: A new approach to object-oriented middleware. IEEE Internet Comput. 8(1), 66–75 (2004)

    Article  MathSciNet  Google Scholar 

  57. Salehi, A.: Design and implementation of an efficient data stream processing system, Doctoral dissertation, cole Polytechnique Fdrale de Lausanne (2010)

    Google Scholar 

  58. Kifer, D., Ben-David, S., Gehrke, J.: Detecting change in data streams. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol. 30, pp. 180–191. VLDB Endowment, August 2004

    Google Scholar 

  59. Compton, M., Barnaghi, P., Bermudez, L., Garcia-Castro, R., Corcho, O., Cox, S., et al.: The SSN ontology of the W3C semantic sensor network incubator group. Web Semant. Sci. Serv. Agents World Wide Web 17, 25–32 (2012)

    Article  Google Scholar 

  60. Neuhaus, H., Compton, M.: The semantic sensor network ontology. In: AGILE Workshop on Challenges in Geospatial Data Harmonisation, Hannover, Germany, pp. 1–33 (2009)

    Google Scholar 

  61. Ashman, J.J.: Multiple chronic conditions among US adults who visited physician offces: data from the National Ambulatory Medical Care Survey, 2009. Preventing Chronic Dis. 10 (2013)

    Google Scholar 

  62. Harbers, M.M., Achterberg, P.W.: Information, Indicators and Data on the Prevalence of Chronic Diseases in the European Union. RIVM, Bilthoven (2012)

    Google Scholar 

  63. Veazie, P.J.: An individual-based framework for the study of medical error. Int. J. Qual. Health Care 18(4), 314–319 (2006)

    Article  Google Scholar 

  64. Lisby, M., Nielsen, L.P., Mainz, J.: Errors in the medication process: frequency, type, and potential clinical consequences. Int. J. Qual. Health Care 17(1), 15–22 (2005)

    Article  Google Scholar 

  65. Lewis, P.J., Dornan, T., Taylor, D., Tully, M.P., Wass, V., Ashcroft, D.M.: Prevalence, incidence and nature of prescribing errors in hospital inpatients. Drug Saf. 32(5), 379–389 (2009)

    Article  Google Scholar 

  66. Lotfi, A., Langensiepen, C., Mahmoud, S.M., Akhlaghinia, M.J.: Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour. J. Ambient Intell. Humanized Comput. 3(3), 205–218 (2012)

    Article  Google Scholar 

  67. Kamei, T.: Information and communication technology for home care in the future. Jpn. J. Nurs. Sci. 10(2), 154–161 (2013)

    Article  Google Scholar 

  68. Kahn, J.M.: Virtual visits - confronting the challenges of telemedicine. N. Engl. J. Med. 372(18), 1684–1685 (2015)

    Article  Google Scholar 

  69. Weinstein, R.S., Lopez, A.M., Joseph, B.A., Erps, K.A., Holcomb, M., Barker, G.P., Krupinski, E.A.: Telemedicine, telehealth, and mobile health applications that work: opportunities and barriers. Am. J. Med. 127(3), 183–187 (2014)

    Article  Google Scholar 

  70. Amendola, S., Lodato, R., Manzari, S., Occhiuzzi, C., Marrocco, G.: RFID technology for IoT-based personal healthcare in smart spaces. IEEE Internet of Things J. 1(2), 144–152 (2014)

    Article  Google Scholar 

  71. Vasquez, A., Huerta, M., Clotet, R., González, R., Rivas, D., Bautista, V.: Using NFC technology for monitoring patients and identification health services. In: Braidot, A., Hadad, A. (eds.) CLAIB 2014. IFMBE Proceedings, vol. 49, pp. 805–808. Springer, Cham (2015)

    Google Scholar 

  72. Bates, D.W., Saria, S., Ohno-Machado, L., Shah, A., Escobar, G.: Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff. 33(7), 1123–1131 (2014)

    Article  Google Scholar 

  73. Kuo, M.H., Sahama, T., Kushniruk, A.W., Borycki, E.M., Grunwell, D.K.: Health big data analytics: current perspectives, challenges and potential solutions. Int. J. Big Data Intell. 1(1–2), 114–126 (2014)

    Article  Google Scholar 

  74. Chen, M., Ma, Y., Song, J., Lai, C.F., Hu, B.: Smart clothing: connecting human with clouds and big data for sustainable health monitoring. Mob. Netw. Appl. 21(5), 825–845 (2016)

    Article  Google Scholar 

  75. Obermeyer, Z., Emanuel, E.J.: Predicting the future - Big Data, machine learning, and clinical medicine. N. Engl. J. Med. 375(13), 1216–1219 (2016)

    Article  Google Scholar 

  76. Bonis, P.: Clinical decision support technology: saving lives. Clin. Serv. J. (2016)

    Google Scholar 

  77. Ku, W.Y., Chou, T.Y., Chung, L.K.: The cloud-based sensor data warehouse. In: Proceedings of ISGC 2011 & OGF 31, vol. 75 (2011)

    Google Scholar 

  78. Yu, H., Wang, D.: Research and implementation of massive health care data management and analysis based on Hadoop. In: 2012 Fourth International Conference on Computational and Information Sciences (ICCIS), pp. 514–517. IEEE, August 2012

    Google Scholar 

  79. Archenaa, J., Anita, E.A.M.: Interactive Big Data management in healthcare using Spark. In: Vijayakumar, V., Neelanarayanan, V. (eds.) ISBCC 2016. SIST, vol. 49, pp. 265–272. Springer, Cham (2016). doi:10.1007/978-3-319-30348-2_21

    Google Scholar 

  80. Jin, Z., Chen, Y.: Telemedicine in the Cloud Era: prospects and challenges. IEEE Pervasive Comput. 14(1), 54–61 (2015)

    Article  Google Scholar 

  81. Yadav, S., Chappar, V., Datir, S., Jagtap, P.: An overview of a pervasive and personalized smart health-care system using IoT. Int. Educ. Res. J. 2(11) (2016)

    Google Scholar 

  82. Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2(1), 1 (2014)

    Article  Google Scholar 

  83. Lokkerbol, J., Adema, D., Cuijpers, P., Reynolds, C.F., Schulz, R., Weehuizen, R., Smit, F.: Improving the cost-effectiveness of a healthcare system for depressive disorders by implementing telemedicine: a health economic modeling study. Am. J. Geriatr. Psychiatry 22(3), 253–262 (2014)

    Article  Google Scholar 

  84. Fernández-Alemán, J.L., Senor, I.C., Lozoya, P.O., Toval, A.: Security and privacy in electronic health records: a systematic literature review. J. Biomed. Inform. 46(3), 541–562 (2013)

    Article  Google Scholar 

  85. Steinhubl, S.R., Muse, E.D., Topol, E.J.: The emerging field of mobile health. Sci. Transl. Med. 7(283), 283rv3 (2015)

    Article  Google Scholar 

  86. Garrety, K., McLoughlin, I., Zelle, G.: Disruptive innovation in health care: business models, moral orders and electronic records. Soc. Policy Soc. 13(04), 579–592 (2014)

    Article  Google Scholar 

  87. Kaldoudi, E., Drosatos, G., Portokallidis, N., Third, A.: An ontology based scheme for formal care plan meta-description. In: Kyriacou, E., Christofides, S., Pattichis, C.S. (eds.) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IP, vol. 57, pp. 785–790. Springer, Cham (2016). doi:10.1007/978-3-319-32703-7_153

    Chapter  Google Scholar 

  88. Rong, C., Nguyen, S.T., Jaatun, M.G.: Beyond lightning: a survey on security challenges in cloud computing. Comput. Electr. Eng. 39(1), 47–54 (2013)

    Article  Google Scholar 

  89. Shrestha, N.M., Alsadoon, A., Prasad, P.W.C., Hourany, L., Elchouemi, A.: Enhanced e-health framework for security and privacy in healthcare system. In: 2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC), pp. 75–79. IEEE, April 2016

    Google Scholar 

  90. Murdoch, T.B., Detsky, A.S.: The inevitable application of Big Data to health care. JAMA 309(13), 1351–1352 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Javier Medina-Quero .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Peláez, M.D., López-Medina, M., Espinilla, M., Medina-Quero, J. (2017). Key Factors for Innovative Developments on Health Sensor-Based System. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10209. Springer, Cham. https://doi.org/10.1007/978-3-319-56154-7_59

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-56154-7_59

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56153-0

  • Online ISBN: 978-3-319-56154-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics