Characteristics of Gait Event and Muscle Activation Parameters of the Lower Limb on the Affected Side in Patients With Hemiplegia After Stroke: A Pilot Study
- PMID: 38163027
- PMCID: PMC10757156
- DOI: 10.1016/j.arrct.2023.100274
Characteristics of Gait Event and Muscle Activation Parameters of the Lower Limb on the Affected Side in Patients With Hemiplegia After Stroke: A Pilot Study
Abstract
Objectives: To confirm the characteristics of gait events and muscle activity in the lower limbs of the affected and unaffected sides in patients with hemiplegia.
Design: Cross-sectional study.
Setting: Motion analysis laboratory of the Wonkwang University Gwangju Hospital.
Participants: Outpatients, diagnosed with ischemic stroke more than 3 months and less than 9 months before participating in the study (N=29; 11 men, 18 women).
Interventions: Not applicable.
Main outcome measures: The gait event parameters and time- and frequency-domain electromyogram (EMG) parameters of the lower limbs of the affected and unaffected sides was determined using BTS motion capture with the Delsys Trigno Avanti EMG wireless system.
Results: The swing time, stance phase, swing phase, single support phase, and median power frequency of the gastrocnemius muscle showed a significant difference between the affected and unaffected sides. Using a logistic regression model, the swing phase, single support phase, and median frequency of the gastrocnemius muscle were selected to classify the affected side.
Conclusion: The single support phase of the affected side is shortened to reduce load bearing, which causes a reduction in the stance phase ratio. Unlike gait-event parameters, EMG data of hemiplegic stroke patients are difficult to generalize. Among them, the logistic regression model with some affected side parameters expected to be set as the severity and improvement baseline of the affected side. Additional data collection and generalization of muscle activity is required to improve the classification model.
Keywords: Electromyography; Hemiplegia; Logistic model; Rehabilitation; Stroke.
© 2023 The Authors.
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