AiCarePWP: Deep learning-based novel research for Freezing of Gait forecasting in Parkinson.

Journal: Computer methods and programs in biomedicine
Published Date: Jun 7, 2024

Abstract

BACKGROUND AND OBJECTIVES: Episodes of Freezing of Gait (FoG) are among the most debilitating motor symptoms of Parkinson's Disease (PD), leading to falls and significantly impacting patients' quality of life. Accurate assessment of FoG by neurologists provides crucial insights into patients' conditions and disease symptoms. This proposed strategy involves utilizing a Weighted Fuzzy Logic Controller, Kalman Filter, and Kaiser-Meyer-Olkin test to detect the gait parameters while walking, resting, and standing phases. Parameters such as neuromodulation format, intensity, duration, frequency, and velocity are computed to pre-empt freezing episodes, thus aiding their prevention.

Authors

  • Hemant Ghayvat
    Computer Science Department, Faculty of Technology, Linnaeus University, P G Vejdes väg, 351 95 Växjö, Sweden.
  • Muhammad Awais
    College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
  • Rebakah Geddam
    Computer Science Department, Institute of Technology, Nirma University, Ahmedabad, 382481, Gujarat, India. Electronic address: rebakah.geddam@nirmauni.ac.in.
  • Muhammad Ahmed Khan
    Department of Health Technology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.
  • Lewis Nkenyereye
    Department of Computer and Information Security, Sejong University, South Korea. Electronic address: nkenyele@sejong.ac.kr.
  • Giancarlo Fortino
    Department of Informatics, Modeling, Electronics and Systems, University of Calabria, 87036 Rende CS, Italy.
  • Kapal Dev
    Department of Institute of Intelligent Systems, University of Johannesburg, Johannesburg, South Africa.

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