Classification of Stroke Severity Using Clinically Relevant Symmetric Gait Features Based on Recursive Feature Elimination With Cross-Validation

Classification of Stroke Severity Using Clinically Relevant Symmetric Gait Features Based on Recursive Feature Elimination With Cross-Validation

Stroke is a leading cause of disability among elderly individuals, and gait impairment is a typical characteristic related to the stroke severity experienced by patients. The aim of this study is to propose a novel stroke severity classification method using symmetric gait features with recursive fe...

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Bibliographic Details
Main Authors: Joohwan Sung, Sungmin Han, Heesu Park, Soree Hwang, Song Joo Lee, Jong Woong Park, Inchan Youn
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Machine learning
assessment of stroke severity
symmetric gait data
feature selection
rehabilitation
Online Access:https://ieeexplore.ieee.org/document/9932572/

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