Case Studies

  • Evangelos B. Mazomenos
  • Juan Mario Rodríguez
  • Carlos Cavero Barca
  • Gennaro Tartarisco
  • Giovanni Pioggia
  • Božidara Cvetković
  • Simon Kozina
  • Hristijan Gjoreski
  • Mitja Lustrek
  • Hector Solar
  • Domen Marincic
  • Jure Lampe
  • Silvio Bonfiglio
  • Koushik Maharatna
Chapter

Abstract

Information and Communication Technologies—as analyzed in this book—could allow a radical change in the way healthcare services are delivered to the citizens and could represent an effective tool to cope with the today’s healthcare challenges.

In this chapter we introduce two European research projects where large part of the concepts addressed in this book are applied; they are the MICHELANGELO project of the seventh Framework Program and CHIRON of the ARTEMIS JU Program.

The CHIRON project (Cyclic and person-centric health management: Integrated approach for home, mobile and clinical environments) focuses on prevention i.e. on a move away from ‘health care’ towards ‘health management’, from ‘how to treat patients’ to ‘how to keep people healthy’, from a “reactive care” to a “proactive care”. CHIRON designed a system’s architecture making possible a “continuum of care” i.e. an integrated health management approach in which health is patient-centric at home, in the hospital and in nomadic environments. Care is moved from the hospital to the home and the healthcare staff is enlarged by adding informal carers to the medical professionals and by motivating and empowering the patient himself to manage his own health. Moreover the CHIRON system builds a personalized risk assessment of the patient by integrating personal information, data gathered at home and in a mobile environment through an innovative set of wearable sensors and data available at the hospital including outcomes of image-based tests. The expected results are a reduction of the healthcare costs and a better quality of care.

MICHELANGELO addresses a specific category of patients i.e. the autistic children; the aim is to use ICT to promote and facilitate the assessment of autism within the home setting, away from the traditional clinical environments and to provide personalized “home-based” intervention strategies. This is achieved through the provision of cost-effective, patient-centric home-based intervention remotely controlled by the therapist (remote rehabilitation). The proposed method aims at enhancing the effectiveness of the treatment through its “intensiveness” and “personalization” matching the individual characteristics of autistic children and the involvement of the parents in their “natural” home environment in the role of “co-therapists”.

Both projects offer interesting inputs on how Information and Communication Technology could help in “revolutionizing” healthcare. It is worthwhile to highlight that both projects keep the doctors at the core of the healthcare process and in both of them technology is not replacing the experience and the competences of the medical professionals and is not removing the needed physical contact between them and the patients but it supports the doctors in executing their tasks in a more effective and better way.

This chapter is split into two parts: in the first we will introduce the two projects mainly from a strategic perspective in line with the current efforts towards “radical changes” needed to cope with the heavy challenges the healthcare system is facing.

The second part gives a technological insight of the CHIRON project and shows how this project is deploying several of the concepts analyzed in the previous chapters of this book.

This part presents the architecture of an integrated continuous monitoring system for Cardiovascular Disease (CVD) patients in nomadic settings developed under the ARTEMIS-JU CHIRON Project. The proposed sensor platform constitutes of commercially available subsystems effectively integrated into a single multi-sensor non-invasive wearable solution. To enable medical experts to assess the patient’s condition remotely, a number of analysis algorithm were developed and implemented into an Android application in order to provide the desired medical information. The key challenge in the development of these algorithmic solutions, was to balance the expected performance while maintaining a low level of power consumption, thus facilitating the continuous monitoring purpose of the system. Furthermore, a web-server based framework provides medical experts with an interactive analysis and monitoring interface and provides the infrastructure for storing the obtained data.

References

  1. Altemeier WA., Altemeier LE. (2009), How can early, intensive training help a genetic disorder? Pediatr Ann, 38:167–170Google Scholar
  2. ARTEMIS Joint Undertaking (2010) The book of projects, pp 56–61. www.artemis-ju.eu/publications
  3. ARTEMIS Joint Undertaking (2011) ARTEMIS strategic research agenda. www.artemis-ju.eu
  4. Atladottier HO et al (2007) Time trends in reported diagnoses of childhood neuropsychiatric disorders: a Danish cohort study. Arch Pediatr Adolesc Med 161(2):193–198CrossRefGoogle Scholar
  5. Barca CC, Rodriguez JM, Rugnone A et al (2012) Medical expert support tool (MEST): a person-centric approach for healthcare management. In: 2012 international conference on smart homes and health telematics (ICOST), Artiminio, pp 99–106Google Scholar
  6. Biswas D, Mazomenos EB, Maharatna K (2012) ECG compression for remote healthcare systems using selective thresholding based on energy compaction. In: 2012 international symposium on signals, systems, and electronics (ISSSE), Seoul, 3–5 Oct 2012, pp 1-6Google Scholar
  7. Bonfiglio S (2010) The CHIRON project. Artemis Mag 7:35–38Google Scholar
  8. Bonfiglio S (2011) CHIRON…Fostering a continuum of care. Artemis Mag 10:24–27Google Scholar
  9. Burns A, Greene BR, McGrath MJ, O’Shea TJ, Kuris B, Ayer SM, Stroiescu F, Cionca V (2010) SHIMMER™—a wireless sensor platform for noninvasive biomedical research. IEEE Sens J 10(9):1527–1534CrossRefGoogle Scholar
  10. Dawson G. (2008) Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder. Dev Psychopathol, 20:775–804Google Scholar
  11. Dawson G, Rogers S, Munson J, Smith M, Winter J, Greenson J, Donaldson A, Varley J (2010) Randomized, controlled trial of an intervention for toddlers with autism: the Early Start Denver Model. Pediatrics, 125:17–23Google Scholar
  12. Delaherche E, Chetouani M (2010) Multimodal coordination: exploring relevant features and measures. Presented at SSPW10, 29 Oct 2010, Firenze, ItalyGoogle Scholar
  13. Dickstein K, Cohen-Solal A, Filippatos G et al (2008) ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2008: the task force for the diagnosis and treatment of acute and chronic heart failure 2008 of the European society of cardiology. Eur Heart J 29:2388–2442CrossRefGoogle Scholar
  14. European Autism Information System (EAIS) (2006–2008) Final project report (Note: The EAIS Project was promoted by the European Autism Alliance (EAA) and funded by the European Commission)Google Scholar
  15. Fielding RT (2000) Architectural styles and the design of network-based software architectures. Ph.D. Dissertation, University of California, IrvineGoogle Scholar
  16. Frost & Sullivan (2009) Preparing for an aging society: challengers faced by healthcare system in European Union, Japan and United States, 2009 [Online] http://www.frost.com/
  17. Gjoreski H (2011) Adaptive human activity recognition and fall detection using wearable sensors. MSc Thesis, Jožef Stefan International Postgraduate SchoolGoogle Scholar
  18. Klersy C, De Silvestri A, Gabutti G et al (2009) A meta-analysis of remote monitoring of heart failure patients. J Am Coll Cardiol 54:1683–1694CrossRefGoogle Scholar
  19. Knapp M, Romeo R, Beecham J (2007) The economic consequences of autism in the UK. Foundation for People with Learning Disabilities, LondonGoogle Scholar
  20. Kononenko I (1994) Estimating attributes: analysis and extensions of RELIEF. In: European conference on machine learning, Catania, pp 171–182Google Scholar
  21. Kuehn BM (2007) Autism spectrum disorders common. JAMA 297(9):940CrossRefGoogle Scholar
  22. Laibow RE, Stubblebine AN, Sandground H, Bounias M (2001) EEG NeuroBioFeedback treatment of patients with brain injury—Part 3: cardiac parameters and finger temperature changes associated with rehabilitation. Presented at the 2001 Society for Neuronal Regulation, ninth annual conference, Monterey, CAGoogle Scholar
  23. Levis P, Madden S, Polastre J, Szewczyk R, Whitehouse K, Woo A, Gay D, Hill J, Welsh M, Brewer E, Culler D (2005) TinyOS: an operating system for sensor networks. In: Weber W, Rabaey JM, Aarts E (eds) Ambient intelligence. Springer, Berlin, pp 115–148, Chapter 7 CrossRefGoogle Scholar
  24. Luštrek M, Cvetković B, Kozina S (2012) Energy expenditure estimation with wearable accelerometers. IEEE international symposium on circuits and systems, Seoul, pp 5–8Google Scholar
  25. Mazomenos EB, Biswas D, Acharyya A, Chen T, Maharatna K, Rosengarten J, Morgan J, Curzen N (2013) A low-complexity ECG feature extraction algorithm for mobile healthcare applications. IEEE Trans Inf Technol Biomed 17(2):459–469Google Scholar
  26. Monk TH, Buysse DJ, Reynolds CF III, Kupfer DJ, Houck PR (1995) Circadian temperature rhythms of older people. Exp Gerontol 30(5):455–474CrossRefGoogle Scholar
  27. Oberman LM., Pascual-Leone (2008). Cortical plasticity: A proposed mechanism by which genomic factors lead to the behavioral and neurological phenotype of autism spectrum and psychotic-spectrum disorders. Behavioral Brain Sciences, 31:276–277Google Scholar
  28. Pantelopoulos A, Bourbakis NG (2010) A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans Syst Man Cybern C Appl Rev 40(1):1–12Google Scholar
  29. Pediatrics. October 2009 issue. http://pediatrics.aappublications.org/content
  30. Puddu PE, Morgan JM, Torromeo C, Curzen N, Schiariti M, Bonfiglio S (2012) A clinical observational study in the CHIRON project: rationale and expected results. Full paper accepted at ICOST 2012Google Scholar
  31. Remington B, Hastings RP, Kovshoff H, Degli Espinosa F, Jahr E, Brown T, Alsford P, Lemaic M, Ward N (2007) Early intensive behavioral intervention: outcomes for children with autism and their parents after two years. Am J Ment Retard 112(6):418–438CrossRefGoogle Scholar
  32. Sensirion [Online]. http://wwww.sensirion.com
  33. Sund-Levander M, Forsberg C, Wahren LK (2002) Normal oral, rectal, tympanic and axillary body temperature in adult men and women: a systematic literature review. Scand J Caring Sci 16(2):122–128CrossRefGoogle Scholar
  34. Sund-Levander M, Grodzinsky E, Loyd D et al (2004) Errors in body temperature assessment related to individual variation measuring technique and equipment. Int J Nurs Pract 10:216–223CrossRefGoogle Scholar
  35. Tapia EM (2008) Using machine learning for real-time activity recognition and estimation of energy expenditure. Ph.D. Thesis, Massachusetts Institute of TechnologyGoogle Scholar
  36. Žbogar M, Gjoreski H, Kozina S, Luštrek M (2012) Improving accelerometer based activity recognition. In: 15th International multiconference information society, Pisa, pp 167–170Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Evangelos B. Mazomenos
    • 1
  • Juan Mario Rodríguez
    • 2
  • Carlos Cavero Barca
    • 2
  • Gennaro Tartarisco
    • 3
  • Giovanni Pioggia
    • 3
  • Božidara Cvetković
    • 4
  • Simon Kozina
    • 4
  • Hristijan Gjoreski
    • 4
  • Mitja Lustrek
    • 4
  • Hector Solar
    • 5
  • Domen Marincic
    • 6
  • Jure Lampe
    • 6
  • Silvio Bonfiglio
    • 7
  • Koushik Maharatna
    • 1
  1. 1.School of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK
  2. 2.ATOS Research and Innovation (ARI), ATOSMadridSpain
  3. 3.Institute of Clinic Physiology, National Research Council (CNR)PisaItaly
  4. 4.Department of Intelligent SystemsJožef Stefan InstituteLjubljanaSlovenia
  5. 5.CEIT, Parque Tecnológico de San Sebastián Paseo MikeletegiDonostia/San SebastiánSpain
  6. 6.Mobili d.o.oLjubljanaSlovenia
  7. 7.FIMI-BARCOSaronnoItaly

Personalised recommendations