ArticleFrontiers in neurology2022
Characterizing stroke-induced changes in the variability of lower limb kinematics using multifractal detrended fluctuation analysis.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Machine Learning-Based Estimation of Knee Joint Mechanics from Kinematic and Neuromuscular Inputs: A Proof-of-Concept Using the CAMS-Knee Datasets.Bioengineering (Basel, Switzerland) · 2026Article
- REEV SENSE IMUs for Gait Analysis in Stroke: A Clinical Study on Lower Limb Kinematics.Sensors (Basel, Switzerland) · 2025Article
- Biomechanical Assessment Methods Used in Chronic Stroke: A Scoping Review of Non-Linear Approaches.Sensors (Basel, Switzerland) · 2024Article
- Immediate changes in stroke patients' gait following the application of lower extremity elastic strap binding technique.Frontiers in physiology · 2024Article
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4 authors.
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Abstract
Movement variability reflects the adaptation of the neuromuscular control system to internal or external perturbations, but its relationship to stroke-induced injury is still unclear. In this study, the multifractal detrended fluctuation analysis was used to explore the stroke-induced changes in movement variability by analyzing the joint angles in a treadmill-walking task. Eight healthy subjects and ten patients after stroke participated in the experiment, performing a treadmill-walking task at a comfortable speed. The kinematics data of the lower limbs were collected by the motion-capture system, and two indicators, the degree of multifractality (α) and degree of correlation [
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