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A Protocol for Comprehensive Analysis of Gait in Individuals with Incomplete Spinal Cord Injury
Karolinska Inst, Dept Neurobiol Care Sci & Soc, S-14183 Stockholm, Sweden.;Aleris Rehab Stn, R&D Unit, S-16989 Solna, Sweden..ORCID iD: 0000-0001-5540-342X
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Aerospace, moveability and naval architecture. (KTH MoveAbility Lab)ORCID iD: 0000-0003-4571-1984
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Aerospace, moveability and naval architecture. (KTH MoveAbility Lab)ORCID iD: 0000-0002-2232-5258
Karolinska Inst, Dept Neurobiol Care Sci & Soc, S-14183 Stockholm, Sweden.;Aleris Rehab Stn, R&D Unit, S-16989 Solna, Sweden..ORCID iD: 0000-0001-7941-3737
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2024 (English)In: Methods and Protocols, ISSN 2409-9279, Vol. 7, no 3, article id 39Article in journal (Refereed) Published
Abstract [en]

This is a protocol for comprehensive analysis of gait and affecting factors in individuals with incomplete paraplegia due to spinal cord injury (SCI). A SCI is a devastating event affecting both sensory and motor functions. Due to better care, the SCI population is changing, with a greater proportion retaining impaired ambulatory function. Optimizing ambulatory function after SCI remains challenging. To investigate factors influencing optimal ambulation, a multi-professional research project was grounded with expertise from clinical rehabilitation, neurophysiology, and biomechanical engineering from Karolinska Institutet, the Spinalis Unit at Aleris Rehab Station (Sweden's largest center for specialized neurorehabilitation), and the Promobilia MoveAbility Lab at KTH Royal Institute of Technology. Ambulatory adults with paraplegia will be consecutively invited to participate. Muscle strength, sensitivity, and spasticity will be assessed, and energy expenditure, 3D movements, and muscle function (EMG) during gait and submaximal contractions will be analyzed. Innovative computational modeling and data-driven analyses will be performed, including the identification of clusters of similar movement patterns among the heterogeneous population and analyses that study the link between complex sensorimotor function and movement performance. These results may help optimize ambulatory function for persons with SCI and decrease the risk of secondary conditions during gait with a life-long perspective.

Place, publisher, year, edition, pages
MDPI AG , 2024. Vol. 7, no 3, article id 39
Keywords [en]
paraplegia, gait, ambulation, movement analysis, machine learning, EMG, predictive modeling
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:kth:diva-350118DOI: 10.3390/mps7030039ISI: 001256315700001PubMedID: 38804333Scopus ID: 2-s2.0-85197173750OAI: oai:DiVA.org:kth-350118DiVA, id: diva2:1882893
Note

QC 20240708

Available from: 2024-07-08 Created: 2024-07-08 Last updated: 2024-07-08Bibliographically approved

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Truong, Minh Tat NhatWang, RuoliGutierrez-Farewik, Elena

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Forslund, Emelie ButlerTruong, Minh Tat NhatWang, RuoliSeiger, ÅkeGutierrez-Farewik, Elena
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