Technologies for Advanced Gait and Balance Assessments in People with Multiple Sclerosis
Web of Science
AuthorShanahan, CJ; Boonstra, FMC; Lizama, LEC; Strik, M; Moffat, BA; Khan, F; Kilpatrick, TJ; van der Walt, A; Galea, MP; Kolbe, SC
Source TitleFRONTIERS IN NEUROLOGY
PublisherFRONTIERS MEDIA SA
University of Melbourne Author/svan der Walt, Anneke; Moffat, Bradford; Khan, Farees; Kilpatrick, Trevor; Galea, Mary; Kolbe, Scott; Shanahan, Camille; STRIK, MYRTE; BOONSTRA, FREDERIEK; Cofre Lizama, Luis Eduardo
AffiliationMedicine, Dentistry & Health Sciences
Medicine and Radiology
Anatomy and Neuroscience
Document TypeJournal Article
CitationsShanahan, CJ; Boonstra, FMC; Lizama, LEC; Strik, M; Moffat, BA; Khan, F; Kilpatrick, TJ; van der Walt, A; Galea, MP; Kolbe, SC, Technologies for Advanced Gait and Balance Assessments in People with Multiple Sclerosis, FRONTIERS IN NEUROLOGY, 2018, 8
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5799707
Subtle gait and balance dysfunction is a precursor to loss of mobility in multiple sclerosis (MS). Biomechanical assessments using advanced gait and balance analysis technologies can identify these subtle changes and could be used to predict mobility loss early in the disease. This update critically evaluates advanced gait and balance analysis technologies and their applicability to identifying early lower limb dysfunction in people with MS. Non-wearable (motion capture systems, force platforms, and sensor-embedded walkways) and wearable (pressure and inertial sensors) biomechanical analysis systems have been developed to provide quantitative gait and balance assessments. Non-wearable systems are highly accurate, reliable and provide detailed outcomes, but require cumbersome and expensive equipment. Wearable systems provide less detail but can be used in community settings and can provide real-time feedback to patients and clinicians. Biomechanical analysis using advanced gait and balance analysis technologies can identify changes in gait and balance in early MS and consequently have the potential to significantly improve monitoring of mobility changes in MS.
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