Quantifying gait stability based on body segment coordination relationships measured with wireless sensors

SUMMARY: The purpose of this study was to investigate how the relationships between upper body (trunk) and lower body (feet) motion are affected by multiple sclerosis (MS), and how these relationships can be used to characterize gait stability in persons with MS (PwMS). In Aim 1, we determined how segment relationships are affected by changing walking speed and sensory input in PwMS compared to healthy controls. PwMS and healthy age-matched controls walked on a treadmill at a range of speeds while wireless inertial sensors measured foot and trunk acceleration. The ratio of acceleration variability between upper and lower body segments, referred to as the gait stability index (GSI), was used to represent the segment coordination relationship during gait and was compared across all walking trials and groups. In Aim 2, we determined how the GSI was related to pathophysiology, clinical disability, and mobility scales in PwMS. Physiological deficits in PwMS were measured through postural response latencies and somatosensation thresholds. Clinical disability and mobility were measured by self-report fall history and clinical questionnaires. In Aim 3, GSI cutoff values and amount of overground walking needed to separate MS fallers from MS non-fallers were determined using data collected continuous 4-minutes of walking over a 10-meter walkway in the laboratory. RELEVANCE: Falls are a leading cause of non-fatal injury and a significant health problem for persons with multiple sclerosis. The current study utilizes a novel technique to examine how critical relationships between motion of upper and lower body segments respond under normal and challenging conditions, and how the maintenance of these relationships is tied to instability and fall risk. Investigation of these segmental relationships during walking is significant to developing gait assessment methods for any population to monitor stability in daily life, identify risk of future falls, and longitudinally track disease progression or treatment efficacy.

Grantor dc:publisher

University of Kansas

Year dc:date.issued

2018

Author dc:creator

• Craig, Jordan

Advisors dc:contributor.advisor

• Huisinga, Jessie M
• Wilson, Sara E

• Biomechanics
• Accelerometry
• Assessments
• Falls
• Gait
• Multiple sclerosis

Statement dc:rights

• Copyright held by the author.

Language dc:language.iso

en

Dc Identifier Other

http://dissertations.umi.com/ku:16195

OAI identifier oai:identifier

oai:kuscholarworks.ku.edu:1808/27613