Advertisement

Gait Monitoring System for Stroke Prediction of Aging Adults

  • Hongkyu ParkEmail author
  • Seunghee Hong
  • Iqram Hussain
  • Damee Kim
  • Young Seo
  • Se Jin ParkEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 973)

Abstract

Health has become a major concern nowadays. People pass significant amount of time of daily life on walking, moving here and there and so on. Some health complexity happens during walking like heart problem, stroke etc. Stroke patient has unbalanced gait pattern compared to normal person. The Internet of Things (IoT) plays an important role in the development of connected people, which offers cloud connectivity, smartphone integration, safety, security, and healthcare services. Insole Foot Pressure sensor and accelerometer will be attached to the foot for gait speed, foot pressure and other gait pattern. Gait parameters of 68 Stroke patients and 208 Elderly healthy persons have been gathered in Chungnam National University Hospital Rehabilitation Center, Daejeon, South Korea. Gait parameters are foot pressure, gait acceleration etc. Dynafoot2 Insole sensor used for data acquisition. Subjects walked and perform activities like walking, sitting, standing, doing some regular activities during gait data acquisition. Area under curve (AUC) of performance curve for C4.5, SVM, Random Tree, Logistic Regression, LSVM, CART algorithms are 0.98, 0.976, 0.935, 0.909 and 0.906, 0.87 respectively. A gait monitoring system has been proposed for stroke onset prediction for stroke patient. IoT sensors are used to gather gait data and machine learning algorithms are used to classify gait pattern of stroke patient group and normal healthy group. In future, sensors such as EEG, EMG will be used to improve system reliability.

Keywords

Stroke Gait Health monitoring Classification algorithms 

Notes

Acknowledgments

This work was supported by the National Research Council of Science & Technology (NST) grant by the Korea government (MSIP) (No. CRC-15-05-ETRI).

References

  1. 1.
    Lee, I.M., Buchner, D.M.: The importance of walking to public health (in eng). Med. Sci. Sports Exerc. 40(7), S512–S518 (2008). SupplCrossRefGoogle Scholar
  2. 2.
    Clark, D.J., Ting, L.H., Zajac, F.E., Neptune, R.R., Kautz, S.A.: Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke 103(2), 844–857 (2010)Google Scholar
  3. 3.
    Lundstrom, E., Smits, A., Borg, J., Terent, A.: Four-fold increase in direct costs of stroke survivors with spasticity compared with stroke survivors without spasticity: the first year after the event (in eng). Stroke 41(2), 319–324 (2010)CrossRefGoogle Scholar
  4. 4.
    Norrving, B., Kissela, B.: The global burden of stroke and need for a continuum of care. Neurology 80(3) Supplement 2, S5 (2013)CrossRefGoogle Scholar
  5. 5.
    Lakhan, S.E., Kirchgessner, A., Hofer, M.: Inflammatory mechanisms in ischemic stroke: therapeutic approaches (in eng). J. Transl. Med. 7, 97 (2009)Google Scholar
  6. 6.
    Faiz, K.W., Sundseth, A., Thommessen, B., Ronning, O.M.: Patient knowledge on stroke risk factors, symptoms and treatment options (in eng). Vasc Health Risk Manag. 14, 37–40 (2018)CrossRefGoogle Scholar
  7. 7.
    Balaban, B., Tok, F.: Gait disturbances in patients with stroke. PM&R 6(7), 635–642 (2014)Google Scholar
  8. 8.
    Gor-García-Fogeda, M.D., Cano de la Cuerda, R., Carratalá Tejada, M., Alguacil-Diego, I.M., Molina-Rueda, F.: Observational gait assessments in people with neurological disorders: a systematic review. Arch. Phys. Med. Rehabil. 97(1), 131–140 (2016)CrossRefGoogle Scholar
  9. 9.
    Yavuzer, G., Kucukdeveci, A., Arasil, T., Elhan, A.: Rehabilitation of stroke patients: clinical profile and functional outcome (in eng). Am. J. Phys. Med. Rehabil. 80(4), 250–255 (2001)CrossRefGoogle Scholar
  10. 10.
    Ponikowski, P., et al.: 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. 18(8), 891–975 (2016)Google Scholar
  11. 11.
    Benson, L.C., Clermont, C.A., Bošnjak, E., Ferber, R.: The use of wearable devices for walking and running gait analysis outside of the lab: a systematic review. Gait & Posture 63, 124–138 (2018)Google Scholar
  12. 12.
    Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (IoT): a vision, architectural elements, and future directions. Future Gen. Comput. Syst. 29(7), 1645–1660 (2013)CrossRefGoogle Scholar
  13. 13.
    Hassanalieragh, M., et al.: Health monitoring and management using internet-of-things (IoT) sensing with cloud-based processing: opportunities and challenges. In: 2015 IEEE International Conference on Services Computing, pp. 285–292 (2015)Google Scholar
  14. 14.
    Park, S.J., Hong, S., Kim, D., Hussain, I., Seo, Y.: Intelligent In-Car Health Monitoring System for Elderly Drivers in Connected Car, Cham, pp. 40–44. Springer International Publishing (2019)Google Scholar
  15. 15.
    Park, S.J., et al.: Development of a real-time stroke detection system for elderly drivers using quad-chamber air cushion and IoT devices (2018).  https://doi.org/10.4271/2018-01-0046
  16. 16.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Korea Research Institute of Standards and ScienceDaejeonSouth Korea
  2. 2.Electronics Telecommunication Research InstituteDaejeonSouth Korea
  3. 3.University of Science & TechnologyDaejeonSouth Korea

Personalised recommendations