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Gait analysis is a well-established approach to detecting motor dysfunction in neurodegenerative disorders like Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS). Most research has targeted spatiotemporal gait variability; however, demographic factors such as age, gender, height, weight, and BMI have been poorly explored. This study combines demographic characteristics with gait descriptors by employing machine learning to enhance classification performance. The results point to the clinical potential of demographic-conscious models for scalable, early, and interpretable diagnostics.

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Human Gait Analysis for Neurodegenerative Diseases

  • 会议论文

  • 发表日期：

    2026年

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  • A. K. Mohapatra,
  • Dimple Sethi,
  • Priya Malik,
  • Neetika Tandon,
  • Hiya Singh,
  • Amisha Trikha

摘要

Gait analysis is a well-established approach to detecting motor dysfunction in neurodegenerative disorders like Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS). Most research has targeted spatiotemporal gait variability; however, demographic factors such as age, gender, height, weight, and BMI have been poorly explored. This study combines demographic characteristics with gait descriptors by employing machine learning to enhance classification performance. The results point to the clinical potential of demographic-conscious models for scalable, early, and interpretable diagnostics.