
Markerless Video-based Gait Analysis in People with Multiple Sclerosis
Author(s) -
Matteo Moro,
Giorgia Marchesi,
Maria Cellerino,
Giacomo Boffa,
Francesca Odone,
Matilde Inglese,
Maura Casadio
Publication year - 2025
Publication title -
ieee transactions on neural systems and rehabilitation engineering
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 1.093
H-Index - 140
eISSN - 1558-0210
pISSN - 1534-4320
DOI - 10.1109/tnsre.2025.3589765
Subject(s) - bioengineering , computing and processing , robotics and control systems , signal processing and analysis , communication, networking and broadcast technologies
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 (EDSS ≥ 3) and "low" disability (EDSS < 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.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom