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)


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.


Stroke Gait Health monitoring Classification algorithms 



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


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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

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