Classification of Parkinson's disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach.

Journal: Journal of neuroengineering and rehabilitation
PMID: 32917244

Abstract

BACKGROUND: Parkinson's disease (PD) and essential tremor (ET) are movement disorders that can have similar clinical characteristics including tremor and gait difficulty. These disorders can be misdiagnosed leading to delay in appropriate treatment. The aim of the study was to determine whether balance and gait variables obtained with wearable inertial motion sensors can be utilized to differentiate between PD and ET using machine learning. Additionally, we compared classification performances of several machine learning models.

Authors

  • Sanghee Moon
    Department of Physical Therapy and Rehabilitation Science, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, USA.
  • Hyun-Je Song
    Naver Search, Naver Corporation, 6 Buljeong-ro, Bundang-gu, Seongnam 13561, Gyeonggi, Republic of Korea.
  • Vibhash D Sharma
    Department of Neurology, University of Kansas Medical Center, Kansas City, KS, USA.
  • Kelly E Lyons
    Department of Neurology, University of Kansas Medical Center, Kansas City, KS, USA.
  • Rajesh Pahwa
    Department of Neurology, University of Kansas Medical Center, Kansas City, KS, USA.
  • Abiodun E Akinwuntan
    Department of Physical Therapy and Rehabilitation Science, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, USA.
  • Hannes Devos
    Department of Physical Therapy and Rehabilitation Science, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, USA.

Keywords

External Resources

Popular Topics
Recent Journals