Short bouts of gait data and inertial sensors can provide reliable measures of spatiotemporal gait parameters from bilateral gait data of participants with multiple sclerosis
|Title:||Short bouts of gait data and inertial sensors can provide reliable measures of spatiotemporal gait parameters from bilateral gait data of participants with multiple sclerosis||Authors:||Ader, Lilian Genaro Motti; Greene, Barry R.; McManus, Killian; Tubridy, Niall; Caulfield, Brian||Permanent link:||http://hdl.handle.net/10197/12218||Date:||13-May-2020||Online since:||2021-05-27T11:11:27Z||Abstract:||Background: Wearable devices equipped with inertial sensors enable objective gait assessment for persons with multiple sclerosis (MS), with potential use in ambulatory care or home and community-based assessments. However, gaitdata collected in non-controlled settings is often fragmented and may not provide enough information forreliable measures. We evaluate a novel approach, extracting pre-defined numbers of gait cycles from the fulllength of a walking task, and their effects on the reliability of spatiotemporal gait parameters. Methods: The present study evaluates intra-session reliability of spatiotemporal gait parameters for short bouts of gaitdata extracted from the full length of the walking tasks to 1) determine the effects of the length of the walkingtask on the reliability of calculated measures and 2) identify spatiotemporal gait parameters that can providereliable measures for gait assessments and reference data in different settings. Thirty-seven participants (37) diagnosed with relapsing-remitting MS (EDSS rage 0 to 4.5) executed two trials,walking 20m each, with inertial sensors attached to their right and left shanks. Previously published algorithms were applied to identify gait events from the medio-lateral angular velocity. Short bouts of gait data wereextracted from each trial, with lengths varying from 3 to 9 gait cycles. Twenty-one measures of spatiotemporalgait parameters were calculated. Intraclass correlation coefficients (ICCs) were calculated to evaluate how the degree of agreement between the two trials of each participant varied with the number of gait cycles included inthe analysis. Results: Spatiotemporal gait parameters calculated as the mean across included gait cycles reach excellent reliabilityfrom three gait cycles. Stride time variability and asymmetry, as well as stride velocity variability and asymmetry, reach good reliability from six gait cycles and should be further explored for persons with MS, whilestride time asymmetry and step time asymmetry do not seem to provide reliable measures and should bereported carefully. Conclusion: Short bouts of gait data, including at least six gait cycles of bilateral data, can provide reliable gait measurements for persons with MS, opening new perspectives for gait assessment using wearable devices in non-controlled environments, to support monitoring of symptoms of persons with neurological diseases.||Funding Details:||Science Foundation Ireland||Funding Details:||Insight Research Centre||Type of material:||Journal Article||Publisher:||BMJ||Journal:||Journal of NeuroEngineering and Rehabilitation||Keywords:||Personal sensing; Gait analysis; Reliability; Body-worn sensors; Multiple sclerosis||DOI:||10.21203/rs.3.rs-27071/v1||Language:||en||Status of Item:||Not peer reviewed||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by/3.0/ie/|
|Appears in Collections:||Public Health, Physiotherapy and Sports Science Research Collection|
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