Cognitive driven gait freezing phase detection and classification for neuro-rehabilitated patients using machine learning algorithms.

Journal: Journal of neuroscience methods
PMID: 38834145

Abstract

BACKGROUND: The significance of diagnosing illnesses associated with brain cognitive and gait freezing phase patterns has led to a recent surge in interest in the study of gait for mental disorders. A more precise and effective way to characterize and classify many common gait problems, such as foot and brain pulse disorders, can improve prognosis evaluation and treatment options for Parkinson patients. Nonetheless, the primary clinical technique for assessing gait abnormalities at the moment is visual inspection, which depends on the subjectivity of the observer and can be inaccurate.

Authors

  • Aditya Khamparia
    Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, India.
  • Deepak Gupta
    Department of Mechanical Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, 248002, India.
  • Mashael Maashi
    Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
  • Hanan Abdullah Mengash
    Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

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