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Remote Healthcare Monitoring for Parkinson’s Disease in a Smart Way

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Internet of Medical Things

Part of the book series: Internet of Things ((ITTCC))

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Abstract

Electronic health is the jargon moving around the healthcare sector. Patient record maintenance in digital format popularly called as electonic health record (EHR). EHR holds information about patients’ demographics, track of visits, and treatments. Information about the patients could be retrieved very shortly and accurately due to digitalization. Another main area to be considered is monitoring and tracking of the sick from the remote. It’s the great challenge facing the present scenario. The global life expectancy has increased swiftly and opened an avenue for a higher level of research that integrates human and machine intelligence. Smart devices from the word clearly indicate the way of its working and performance. The number of industries joint hands with researchers especially doctors and engineers to build the devices that are ergonomically mapping and serving the purpose of providing healthcare to the sick. In our work, we discussed the smart way of handling the alarming neurodegenerative disease named Parkinson’s.

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References

  1. Goldman, S. M., & Tanner, C. (1998). Etiology of Parkinson’s disease. In J. Jankovic & E. Tolosa (Eds.), Parkinson’s disease and movement disorders (3rd ed., pp. 133–158). Baltimore: Williams and Wilkins.

    Google Scholar 

  2. Haaxma, C. A., Bloem, B. R., Borm, G. F., Oyen, W. J. G., Leenders, K. L., Eshuis, S., Booij, J., Dluzen, D. E., & Horstink, M. W. I. M. (2007). Gender differences in Parkinson’s disease. Journal of Neurology, Neurosurgery & Psychiatry, 78(8), 819–824.

    Article  Google Scholar 

  3. Bailey, M., Anderson, S., & Hall, D. A. (2020). Parkinson’s disease in African Americans: A review of the current literature. Journal of Parkinson’s Disease, 831–841.

    Google Scholar 

  4. Kim, K. S. (2017). Toward neuroprotective treatments of Parkinson’s disease. Proceedings of the National Academy of Sciences of the United States of America, 114(15), 3795–3797.

    Article  Google Scholar 

  5. Zhang, Z. X., Roman, G. C., Hong, Z., Wu, C. B., Qu, Q. M., Huang, J. B., et al. (2005). Parkinson’s disease in China: prevalence in Beijing, Xian, and Shanghai. Lancet, 365(9459), 595–597.

    Article  Google Scholar 

  6. Dorsey, E. R., Constantinescu, R., Thompson, J. P., Biglan, K. M., Holloway, R. G., Kieburtz, K., et al. (2007). Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology, 68(5), 384.

    Article  Google Scholar 

  7. Tai, Y. C., & Lin, C. H. (2020). An overview of pain in Parkinson’s disease. Clinical Parkinsonism & Related Disorders, 2, 1–8.

    Article  Google Scholar 

  8. Chaudhuri, K. R., Rizos, A., Trenkwalder, C., Rascol, O., Pal, S., Martino, D., et al. (2015). King’s Parkinson’s disease pain scale, the first scale for pain in PD: An international validation. Movement Disorders, 30(12), 1623–1631.

    Article  Google Scholar 

  9. Skogar, O., & Lokk, J. (2016). Pain management in patients with Parkinson’s disease: Challenges and solutions. Journal of Multidisciplinary Healthcare, 9, 469.

    Article  Google Scholar 

  10. Diederich, N. J., Uchihara, T., Grillner, S., & Goetz, C. G. (2020). The evolution-driven signature of Parkinson’s disease. Trends in Neurosciences.

    Google Scholar 

  11. Mirelman, A., Bernad-Elazari, H., Thaler, A., Giladi-Yacobi, E., Gurevich, T., Gana-Weisz, M., et al. (2016). Arm swing as a potential new prodromal marker of Parkinson’s disease. Movement Disorders, 31(10), 1527–1534.

    Article  Google Scholar 

  12. Ha, A. D., & Jankovic, J. (2012). Pain in Parkinson’s disease. Movement Disorders, 27(4), 485–491.

    Article  Google Scholar 

  13. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing (NIST special publication 800–145). National Institute of Standards and Technology NIST.

    Google Scholar 

  14. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., Konwinski, A., Lee, G., Patterson, D. A., Rabkin, A., Stoica, I., & Zaharia, M. (2009). Above the clouds: A Berkeley view of cloud computing. February, 10.

    Google Scholar 

  15. Appel-Cresswell, S., Doudet, D., Sossi, V., et al. (2011). Functional neuroimaging in Parkinson’s disease. Expert Opinion on Medical Diagnostics, 5(2), 109–120.

    Article  Google Scholar 

  16. Kim, B., Lee, W. W., Kim, A., Lee, H. J., Park, H. Y., Jeon, H. S., Kim, S. K., Jeon, B., & Park, K. S. (2018). Wrist sensor-based tremor severity quantification in Parkinson’s disease using convolutional neural network. Computers in Biology and Medicine, 95, 140–146.

    Article  Google Scholar 

  17. El Maachi, I., Bilodeau, G.-A., & Bouachir, W. (2020). Deep 1d-convnet for accurate Parkinson disease detection and severity prediction from gait. Expert Systems with Applications, 143, 113075.

    Article  Google Scholar 

  18. Robichaud, J. A., Pfann, K. D., Leurgans, S., Vaillancourt, D. E., Comella, C. L., & Corcos, D. M. (2009). Variability of EMG patterns: A potential neurophysiological marker of Parkinson’s disease? Clinical Neurophysiology, 120(2), 390–397.

    Article  Google Scholar 

  19. Zhao, A., Qi, L., Li, J., Dong, J., & Yu, H. (2018). A hybrid spatio-temporal model for detection and severity rating of Parkinson’s disease from gait data. Neurocomputing, 315, 1–8.

    Article  Google Scholar 

  20. Belić, M., Bobić, V., Badža, M., Šolaja, N., Ðurić-Jovičić, M., & Kostić, V. S. (2019). Artificial intelligence for assisting diagnostics and assessment of Parkinson’s disease–a review. Clinical Neurology and Neurosurgery, 105442.

    Google Scholar 

  21. National Health Service: https://www.nhs.uk/conditions/parkinsons-disease/treatment/. Accessed 26 Sept 2020.

  22. Jankovic, J. (2008). Parkinson’s disease: Clinical features and diagnosis. Journal of Neurology, Neurosurgery & Psychiatry, 79(4), 368–376.

    Article  Google Scholar 

  23. Fahn, S., Elton, R., Marsden, C. D., Calne, D., & Goldstein, M. (1987). The unified Parkison disease rating scale. Recent Developments in Parkinson’s Disease, 2, 153–163.

    Google Scholar 

  24. Pistacchi, M., Gioulis, M., Sanson, F., De Giovannini, E., Filippi, G., Rossetto, F., et al. (2017). Gait analysis and clinical correlations in early Parkinson’s disease. Functional Neurology, 32(1), 28.

    Article  Google Scholar 

  25. Reich, S. G., & Savitt, J. M. (2019). Parkinson’s disease. Medical Clinics of North America. Neurology for the Non-Neurologist, 103(2), 337–350. https://doi.org/10.1016/j.mcna.2018.10.014.

    Article  Google Scholar 

  26. Poongodi, T., Sumathi, D., Suresh, P., & Balusamy, B. (2020). Deep learning techniques for Electronic Health Record (EHR) Analysis (Studies in computational intelligence) (Vol. 903, pp. 73–103). Springer.

    Google Scholar 

  27. El Maachi, I., Bilodeau, G.-A., & Bouachir, W. (2020). Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait. Expert Systems with Applications, Elsevier, 143, 1–7.

    Article  Google Scholar 

  28. Levy, O. A., Malagelada, C., & Greene, L. A. (2009). Cell death pathways in Parkinson’s disease: Proximal triggers, distal effectors, and final steps. Apoptosis: An International Journal on Programmed Cell Death, 14(4), 478–500. https://doi.org/10.1007/s10495-008-0309-3.

    Article  Google Scholar 

  29. Emamzadeh, F. N., & Surguchov, A. (2018). Parkinson’s disease: Biomarkers, treatment, and risk factors. Frontiers in Neuroscience, 12, 612.

    Article  Google Scholar 

  30. He, R., Yan, X., Guo, J., Xu, Q., Tang, B., & Sun, Q. (2018). Recent advances in biomarkers for Parkinson’s disease. Frontiers in Aging Neuroscience, 10, 305.

    Article  Google Scholar 

  31. Mayeux, R. (2004). Biomarkers: Potential uses and limitations. NeuroRx, 1(2), 182–188.

    Article  Google Scholar 

  32. Chen-Plotkin, A. S., Albin, R., Alcalay, R., Babcock, D., Bajaj, V., Bowman, D., et al. (2018). Finding useful biomarkers for Parkinson’s disease. Science Translational Medicine, 10(454).

    Google Scholar 

  33. Koydemir, H. C., & Ozcan, A. (2018). Wearable and implantable sensors for biomedical applications. Annual Review of Analytical Chemistry, 11, 127–146.

    Article  Google Scholar 

  34. Rashidi, P., & Mihailidis, A. (2012). A survey on ambient-assisted living tools for older adults. IEEE Journal of Biomedical and Health Informatics, 17(3), 579–590.

    Article  Google Scholar 

  35. Rovini, E., Maremmani, C., & Cavallo, F. (2017). How wearable sensors can support Parkinson’s disease diagnosis and treatment: A systematic review. Frontiers in Neuroscience, 11, 555.

    Article  Google Scholar 

  36. Botros, A., Schütz, N., Camenzind, M., Urwyler, P., Bolliger, D., Vanbellingen, T., et al. (2019). Long-term home-monitoring sensor technology in patients with Parkinson’s disease—Acceptance and adherence. Sensors, 19(23), 5169.

    Article  Google Scholar 

  37. Reya, M. L.-d., Vela-Desojob, L., & Cano-de la Cuerdaa, C. R. (2019). Mobile phone applications in Parkinson’s disease: A systematic review. Science Direct, 34(1), 38–54.

    Google Scholar 

  38. Son, H., Park, W. S., & Kim, H. (2018). Mobility monitoring using smart technologies for Parkinson’s disease in free-living environment. Collegian, 25(5), 549–560.

    Article  Google Scholar 

  39. Seneviratne, S., Hu, Y., Nguyen, T., Lan, G., Khalifa, S., Thilakarathna, K., … & Seneviratne, A. A survey of wearable devices and challenges. IEEE Communications Surveys & Tutorials, 19(4), 2573–2620, (2017).

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Information Technology & Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    R. Sujatha

  2. School of Computing Science and Engineering, Galgotias University, Delhi, India

    T. Poongodi

  3. PG and Research Department of Computer Science, Cauvery College for Women, Tiruchirappalli, Tamil Nadu, India

    S. Suganthi

Authors
  1. R. Sujatha
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  2. T. Poongodi
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  3. S. Suganthi
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Corresponding author

Correspondence to R. Sujatha .

Editor information

Editors and Affiliations

  1. Department of Electronics & Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

    D. Jude Hemanth

  2. Department of Electronics & Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

    J. Anitha

  3. Department of Informatics, University of Piraeus, Piraeus, Greece

    George A. Tsihrintzis

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Sujatha, R., Poongodi, T., Suganthi, S. (2021). Remote Healthcare Monitoring for Parkinson’s Disease in a Smart Way. In: Hemanth, D.J., Anitha, J., Tsihrintzis, G.A. (eds) Internet of Medical Things. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-63937-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-63937-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63936-5

  • Online ISBN: 978-3-030-63937-2

  • eBook Packages: MedicineMedicine (R0)

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