Evaluating the Intra-limb Coordination during Gait in Hemiplegia

Luo, Haizhen; Luo, Jie

doi:10.1109/cbs.2018.8612239

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…During the experiment, subjects were asked to walk on a treadmill for 3 min at a comfortable speed, and this activity was repeated 3 times. There was a 5-min rest period between each walking experiment (31). When the subject was walking on the treadmill, their threedimensional coordinate trajectory was detected by the 6-cameras motion capture system with a 100 Hz sampling rate (OptiTrack, Natural Point, USA).…”

Section: Experimental Setup and Proceduresmentioning

confidence: 99%

Characterizing stroke-induced changes in the variability of lower limb kinematics using multifractal detrended fluctuation analysis

Wang

et al. 2022

Front. Neurol.

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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 [h(2)], were used to investigate the mechanisms underlying neuromuscular control. The results showed that the knee and ankle joint angles were multifractal and persistent at various scales, and there was a significant difference in the degree of multifractality and the degree of correlation at the knee and ankle joint angles among the three groups, with the values being ranked in the following order: healthy subjects < non-paretic limb < paretic limb. These observations highlighted increased movement variability and multifractal strength in patients after stroke due to neuromotor defects. This study provided evidence that multifractal detrended analysis of the angles of the knee and ankle joints is useful to investigate the changes in movement variability and multifractal after stroke. Further research is needed to verify and promote the clinical applications.

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Section: Experimental Setup and Proceduresmentioning

confidence: 99%

Characterizing stroke-induced changes in the variability of lower limb kinematics using multifractal detrended fluctuation analysis

Wang

et al. 2022

Front. Neurol.

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Gait Phase Partitioning and Footprint Detection Using Mutually Constrained Piecewise Linear Approximation with Dynamic Programming

Yasukawa

Makihara

Hosoi³

et al. 2021

IEICE Trans. Inf. & Syst.

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Automatic Hemiplegia Type Detection (Right or Left) Using the Levenberg-Marquardt Backpropagation Method

et al. 2022

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Hemiplegia affects a significant portion of the human population. It is a condition that causes motor impairment and severely reduces the patient’s quality of life. This paper presents an automatic system for identifying the hemiplegia type (right or left part of the body is affected). The proposed system utilizes the data taken from patients and healthy subjects using the accelerometer sensor from the RehaGait mobile gait analysis system. The collected data undergo a pre-processing procedure followed by a feature extraction stage. The extracted features are then sent to a neural network trained by the Levenberg-Marquardt backpropagation (LM-BP) algorithm. The experimental part of this research involved creating a custom-created dataset containing entries taken from ten healthy and twenty non-healthy subjects. The data were taken from seven different sensors placed in specific areas of the subjects’ bodies. These sensors can capture a three-dimensional (3D) signal using the accelerometer, magnetometer, and gyroscope device types. The proposed system used the signals taken from the accelerometers, which were split into 2-sec windows. The proposed system achieved a classification accuracy of 95.12% and was compared with fourteen commonly used machine learning approaches.

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Evaluating the Intra-limb Coordination during Gait in Hemiplegia

Cited by 3 publications

References 10 publications

Characterizing stroke-induced changes in the variability of lower limb kinematics using multifractal detrended fluctuation analysis

Characterizing stroke-induced changes in the variability of lower limb kinematics using multifractal detrended fluctuation analysis

Gait Phase Partitioning and Footprint Detection Using Mutually Constrained Piecewise Linear Approximation with Dynamic Programming

Automatic Hemiplegia Type Detection (Right or Left) Using the Levenberg-Marquardt Backpropagation Method

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