Background: Gait adaptation serves as the foundation for safe community ambulation in
stroke survivors. However, there is no specific methodology for assessing and diagnosing …

Background Combining machine learning (ML) with gait analysis is widely applicable for
diagnosing abnormal gait patterns. Objective To analyze gait adaptability characteristics in …

Background Gait classification is a clinically helpful task performed after a stroke in order to
guide rehabilitation therapy. Gait disorders are commonly identified using observational gait …

Background The evaluation of gait function and severity classification of stroke patients are
important to determine the rehabilitation goal and the level of exercise. Physicians often …

BACKGROUND: Stroke is one of the major diseases endangering the health of Chinese
citizens, characterized by high morbidity, disability, mortality and recurrence. Gait …

Background Among neurological pathologies, cerebral palsy and stroke are the main
contributors to walking disorders. Machine learning methods have been proposed in the …

Background Although there is evidence that stroke survivors have reduced gait adaptability,
the underlying mechanisms and the relationship to functional recovery are largely unknown …

Background and Purpose—Gait velocity is a powerful indicator of function and prognosis
after stroke. Gait velocity can be stratified into clinically meaningful functional ambulation …

Background: Gait dysfunction or impairment is considered one of the most common and
devastating physiological consequences of stroke, and achieving optimal gait is a key goal …

Introduction: Clinical gait analysis plays a central role in the rehabilitation of stroke patients.
However, practical and technical challenges limit their use in clinical settings. This study …