Data Augmentation for Classifying Multiple Sclerosis Severity through Inertial Measurement Unit-Based Gait Analysis

Gait impairments are highly prevalent in persons with multiple sclerosis (PwMS), contributing to difficulties in daily activities. Disability is commonly assessed using the Expanded Disability Status Scale (EDSS). However, the EDSS lacks detailed gait analysis for assessing the severity of multiple sclerosis. To address this, gait analysis tools such as inertial measurement units are commonly used to understand walking patterns in PwMS. Another concern is that collecting sufficient gait data becomes challenging due to limited participation in studies. This research acknowledges these limitations and proposes using a variational autoencoder to address this issue. Additionally, the study explores the feasibility of classification models aimed at assisting the quantification of disability of PwMS based on individuals’ gait patterns.