Identifying best fall-related balance factors and robotic-assisted gait training attributes in 105 post-stroke patients using clinical machine learning models.

Journal: NeuroRehabilitation
PMID: 39031394

Abstract

BACKGROUND: Despite the promising effects of robot-assisted gait training (RAGT) on balance and gait in post-stroke rehabilitation, the optimal predictors of fall-related balance and effective RAGT attributes remain unclear in post-stroke patients at a high risk of fall.

Authors

  • Heejun Kim
    Department of Physical Therapy, Sports Movement Artificial Robotics Technology (SMART) Institute, Yonsei University, Wonju, Korea.
  • Jiwon Shin
    Radiology Artificial Intelligence Lab (RAIL), Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
  • Yunhwan Kim
    Department of Physical Therapy, Sports Movement Artificial Robotics Technology (SMART) Institute, Yonsei University, Wonju, Korea.
  • Yongseok Lee
    Myongji-Choonhey Rehabilitation Hospital, Seoul, Republic of Korea.
  • Joshua Sung H You
    Department of Physical Therapy, Sports Movement Artificial Robotics Technology (SMART) Institute, Yonsei University, Wonju, Republic of Korea.

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