Markerless Video-Based Gait Analysis in People With Multiple Sclerosis

Gait analysis plays a crucial role in assessing mobility impairments and monitoring disease progression in individuals with Multiple Sclerosis (MS). Markerless, video-based methods offer a non-invasive, practical alternative to traditional marker-based systems, making them particularly suitable for clinical applications. This study employs a markerless video-based approach to extract spatio-temporal and kinematic parameters from 25 individuals with MS and 25 age- and sex-matched unimpaired controls. The MS cohort was divided into two subgroups based on the Expanded Disability Status Scale (EDSS): "high" disability ( $\textit {EDSS} \geq {3}$ ) and "low" disability ( $\textit {EDSS} \lt {3}$ ). Both normal and tandem gait patterns were evaluated. In normal gait, significant spatio-temporal and joint kinematic differences were observed between the high EDSS group and unimpaired controls, while the low EDSS group exhibited no notable deviations. In contrast, tandem gait analysis revealed significant differences in heel-to-toe distance between the low EDSS group and unimpaired controls, highlighting subtle changes that were undetectable in normal gait. These findings underscore the potential of video-based methods to enhance disease monitoring and guide targeted rehabilitation strategies in MS.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Published
2025-01-01
DOI
https://doi.org/10.1109/tnsre.2025.3589765
Citations
3
Primary Topic
Multiple Sclerosis Research Studies
Type
article
Field-Weighted Citation Impact
4.41

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Markerless Video-Based Gait Analysis in People With Multiple Sclerosis

Maria Cellerino, Giorgia Marchesi, Francesca Odone, Maura Casadio et al.
3 citations
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Multiple Sclerosis Research Studies
4.41
article

Markerless Video-Based Gait Analysis in People With Multiple Sclerosis

Maria Cellerino, Giorgia Marchesi, Francesca Odone, Maura Casadio, Giacomo Boffa, Matilde Inglese, Matteo Moro
article en
3 citations

Abstract

Gait analysis plays a crucial role in assessing mobility impairments and monitoring disease progression in individuals with Multiple Sclerosis (MS). Markerless, video-based methods offer a non-invasive, practical alternative to traditional marker-based systems, making them particularly suitable for clinical applications. This study employs a markerless video-based approach to extract spatio-temporal and kinematic parameters from 25 individuals with MS and 25 age- and sex-matched unimpaired controls. The MS cohort was divided into two subgroups based on the Expanded Disability Status Scale (EDSS): "high" disability ( $\textit {EDSS} \geq {3}$ ) and "low" disability ( $\textit {EDSS} \lt {3}$ ). Both normal and tandem gait patterns were evaluated. In normal gait, significant spatio-temporal and joint kinematic differences were observed between the high EDSS group and unimpaired controls, while the low EDSS group exhibited no notable deviations. In contrast, tandem gait analysis revealed significant differences in heel-to-toe distance between the low EDSS group and unimpaired controls, highlighting subtle changes that were undetectable in normal gait. These findings underscore the potential of video-based methods to enhance disease monitoring and guide targeted rehabilitation strategies in MS.

IEEE Transactions on Neural Systems and Rehabilitation EngineeringVol. 33
Ospedale Policlinico San Martino (IT), University of Genoa (IT)
Fondazione Italiana Sclerosi Multipla
Openalex Percentile: Top 6%
Multiple Sclerosis Research Studies
4.41
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.