Skip to main content

Advertisement

Log in

An IoT approach for integration of computational intelligence and wearable sensors for Parkinson’s disease diagnosis and monitoring

  • Review Paper
  • Published:
Health and Technology Aims and scope Submit manuscript

Abstract

Nowadays the continuous growing in global population and the related increase of life expectancy lead to explore new ways of making the most of the limited resources humanity has. This endeavor challenges especially the current health care of elderly population, which is particularly associated with a marked prevalence of chronic neurological disorders such as Parkinson’s Disease. Internet of Things and wearable technologies have opened up a new revolution in the domain of healthcare. Minimizing the response time in diagnosis and treatment, Internet of Things thrives towards omnipresence of the healthcare services. Using wearable devices, the lifestyle data is collected from multifarious sources, which is then accumulated, analyzed and acted upon. The emerging technological area of Wearable Sensors and the Internet of Things seems to provide a smart and intelligent way of catering ubiquitous healthcare services to the elderly population, taking healthcare facilities to a higher dimension of omnipresence.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Parkinson’s UK. Parkinson’s prevalence in the United Kingdom. London, UK; 2012. p. 1–13.

  2. Crawford P, Zimmerman EE. Differentiation and diagnosis of tremor. Am Fam Physician. 2011;1583(6):697–702.

    Google Scholar 

  3. Jankovic J. Parkinson’s disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry. 2008;79:368–76.

    Article  Google Scholar 

  4. DeLong MR, Wichmann T. Circuits and circuit disorders of the basal ganglia. Arch Neurol. 2007;64(1):20–4.

    Article  Google Scholar 

  5. Darkins AW, Fromkin VA, Benson DF. A characterization of the prosodic loss in Parkinson’s disease. Brain Lang. 1988;34:315–32.

    Article  Google Scholar 

  6. Budzianowska A, Honczarenko K. Assessment of rest tremor in Parkinson’s disease. Pol J Neurol Neurosurg. 2008;42:12–21.

    Google Scholar 

  7. Bacher M, Scholz E, Diener HC. 24 hour continuous tremor quantification based on EMG recording. Electroencephalogr Clin Neurophysiol. 1989;72:176–83.

    Article  Google Scholar 

  8. Rajaraman V, Jack D, Adamovich SV, Hening W, Sage J, Poizner H. A novel quantitative method for 3D measurement of parkinsonian tremor. Clin Neurophysiol. 2000;111:338–43.

    Article  Google Scholar 

  9. Salarian RH, Wider C, Burkhard PR, Vingerhoets FJ, Aminian K. Quantification of tremor and bradykinesia in Parkinson’s disease using a novel ambulatory monitoring system. IEEE Trans Biomed Eng. 2007;54(2):313–22.

    Article  Google Scholar 

  10. Pastorino M, Arredondo MT, Cancela J, S. G. Wearable sensor network for health monitoring: The case of Parkinson’s Disease. J Phys Conf Ser. 2013;450(1):012055.IOP

    Article  Google Scholar 

  11. Pasluosta F, Barth J, Gassner H, Klucken J, Eskofier BM. Pull test estimation in parkinson’s disease patients using wearable sensor technology. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015. p. 3109–12.

  12. Foerster F, Smeja M, Fahrenberg J. Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring. Comput Hum Behav. 1999;15:571–83.

    Article  Google Scholar 

  13. Patel S, Hughes R, Huggins N, Standaert D, Growdon J, Dy J, Bonato P. Using wearable sensors to predict the severity of symptoms and motor complications in late stage Parkinson’s disease. In Proc. 30th IEEE Annu. Int Conf Eng Med Biol Soc. Vancouver, BC, Canada; 2008. p. 3686–9.

  14. Patel S, Lorincz K, Hughes R, Huggins N, Growdon J, Standaert D, Akay M, Dy J, Welsh M, Bonato P. Monitoring motor fluctuations in patients with Parkinson’s disease using wearable sensors. IEEE Trans Info Tech Biomed. 2009;13(6):864–73.

    Article  Google Scholar 

  15. Smeja M, Foerster F, Fuchs G, Emmans D, Hornig A, Fahrenberg J. 24–h assessment of tremor activity and posture in Parkinson’s disease by multi-channel accelerometry. J Psychophysiol. 1999;13:245–56.

    Article  Google Scholar 

  16. Lorincz K, Chen BR, Challen GW, Chowdhury AR, Patel S, Bonato P, Welsh M. Mercury: a wearable sensor network platform for high-fidelity motion analysis. Sens Syst. 2009;9:183–96.

    Google Scholar 

  17. Ward JA, Lukowicz P, Troster G, Starner TE. Activity recognition of assembly tasks using body-worn microphones and accelerometers. IEEE Trans Pattern Anal Mach Intell. 2006;28(10):1553–67.

    Article  Google Scholar 

  18. Hoff JI, EA W, BJ v H. Ambulatory objective assessment of tremor in Parkinson’s disease. Clin Neuropharmacol. 2001;24:280–3.

    Article  Google Scholar 

  19. https://nwpf.org/stay-informed/news/2011/12/eyebrain-tracker-to-be-used-in-clinical-trial-forparkinson%E2%80%99s-therapy/

  20. Norman KE, Edwards R, Beuter A. The measurement of tremor using a velocity transducer: comparison to simultaneous recordings using transducers of displacement, acceleration and muscle activity. J Neurosci Methods. 1999;92:41–54.

    Article  Google Scholar 

  21. O’Suilleabhain PE, Matsumoto JY. Time–frequency analysis of tremors. Brain. 1998;121:2127–34.

    Article  Google Scholar 

  22. Rudzinska M, Izworski A, Banaszkiewicz K, Bukowczan S, Marona M, Szczudlik A. Quantitative tremor measurement with the computerized analysis of spiral drawing. Neurol Neurochir Pol. 2007;41:510–6.

    Google Scholar 

  23. Timmer J, Gantert C, Deuschl G, Honerkamp J. Characteristics of hand tremor time series. Biol Cybern. 1993;70:75–80.

    Article  MATH  Google Scholar 

  24. van Someren J, Vonk BF, Thijssen WA, Speelman JD, Schuurman PR, Mirmiran M, Swaab DF. A new actigraph for longterm registration of the duration and intensity of tremor and movement. IEEE Trans Biomed Eng. 1998;45(3):386–95.

    Article  Google Scholar 

  25. Zwartjes HT, van Vugt J, Geelen J, Veltink P. Development of a system for measurement and analysis of tremor using a three-axis accelerometer. IEEE Trans Biomed Eng. 2010;57:2779–86.

    Article  Google Scholar 

  26. O’Suilleabhain PE, Dewey Jr RB. Validation for tremor quantification of an electromagnetic tracking device. Mov Disord. 2001;16:265–71.

    Article  Google Scholar 

  27. Riviere N, Reich SG, Thakor NV. Adaptive fourier modeling for quantification of tremor. J Neurosci Methods. 1997;74:77–87.

    Article  Google Scholar 

  28. Cipparrone L, Ginanneschi A, Degl'Innocenti F, Porzio P, Pagnini P, Marini P. Electro-oculographic routine examination in Parkinson's disease. Acta Neurol Scand. 1988;77:6–11. doi:10.1111/j.1600-0404.1988.tb06966.x.

    Article  Google Scholar 

  29. Lane N, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell A, College D. Adhoc and sensor networks: a survey of mobile phone sensing. IEEE Commun Mag. 2010;140–150

  30. Chatterjee P, Armentano RL. “Internet of things for a smart and ubiquitous eHealth system”, computational intelligence and connected networks (CICN), 2015 seventh international conference on. 2015.

  31. The Michael J. Fox Foundation. Wearable sensors and a web-based application to monitor patients with Parkinson’s disease in the home environment. https://www.michaeljfox.org/foundation/grant-detail.php?grant_id=471

  32. Ganti R, Ye F, Lei H. Mobile crowdsensing: current state and future challenges. IEEE Commun Mag. 2011;49:32–9.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Luis Eduardo Romero or Parag Chatterjee.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Funding

There is no funding source.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

This article is part of the Topical Collection on Health and Technology in Latin America

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Romero, L.E., Chatterjee, P. & Armentano, R.L. An IoT approach for integration of computational intelligence and wearable sensors for Parkinson’s disease diagnosis and monitoring. Health Technol. 6, 167–172 (2016). https://doi.org/10.1007/s12553-016-0148-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12553-016-0148-0

Keywords

Navigation