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Parkinson’s Disease (PD) is a progressive neurodegenerative disorder affecting movement, with gait disturbances being among the earliest and most disabling symptoms. Recent advances in artificial intelligence (AI), particularly in machine learning (ML), have enabled new paradigms in gait analysis, offering improved diagnosis, monitoring, and personalized treatment strategies. This paper presents an implementation of the Random Forest model integrated into a Sktime web application, utilizing data from Videogrametry 2D to measure the angle of flexo-extension of the knee in a sagittal plane, highlighting key challenges and future directions in the practical deployment of AI for gait analysis in Parkinson’s disease (PD).

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Convolutional Neural Network and Random Forest Model Used to Classify Healthy and Parkinson’s Human Gait

  • 会议论文

  • 发表日期：

    2026年

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  • Jocabed Mendoza-Martínez,
  • Fiacro Jiménez-Ponce,
  • Ramsés Hernández-Cerero,
  • Christopher René Torres-SanMiguel

摘要

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder affecting movement, with gait disturbances being among the earliest and most disabling symptoms. Recent advances in artificial intelligence (AI), particularly in machine learning (ML), have enabled new paradigms in gait analysis, offering improved diagnosis, monitoring, and personalized treatment strategies. This paper presents an implementation of the Random Forest model integrated into a Sktime web application, utilizing data from Videogrametry 2D to measure the angle of flexo-extension of the knee in a sagittal plane, highlighting key challenges and future directions in the practical deployment of AI for gait analysis in Parkinson’s disease (PD).