Detailed Information on Publication Record

NAMAZI, Hamidreza. Complexity-based analysis of the correlation between stride interval variability and muscle reaction at different walking speeds. Biomedical Signal Processing and Control. England: Elsevier, 2021, vol. 69, Aug 2021, p. nestránkováno, 13 pp. ISSN 1746-8094. Available from: https://dx.doi.org/10.1016/j.bspc.2021.102956.

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Basic information
Original name	Complexity-based analysis of the correlation between stride interval variability and muscle reaction at different walking speeds
Authors	NAMAZI, Hamidreza (364 Islamic Republic of Iran, guarantor, belonging to the institution).
Edition	Biomedical Signal Processing and Control, England, Elsevier, 2021, 1746-8094.

Other information
Original language	English
Type of outcome	Article in a journal
Field of Study	30306 Sport and fitness sciences
Country of publisher	United Kingdom of Great Britain and Northern Ireland
Confidentiality degree	is not subject to a state or trade secret
WWW	URL
Impact factor	Impact factor: 5.076
RIV identification code	RIV/00216224:14510/21:00121893
Organization unit	Faculty of Sports Studies
Doi	http://dx.doi.org/10.1016/j.bspc.2021.102956
UT WoS	000685643500009
Keywords in English	Muscle reaction; Gait variability; WalkingComplexity; Fractal theory; Sample entropy
Tags	rivok
Tags	International impact, Reviewed
Changed by	Changed by: Mgr. Pavlína Roučová, DiS., učo 169540. Changed: 21/4/2022 15:06.

Abstract
In this research, for the first time, we evaluated the correlation between the variations of leg muscle reaction and gait at different walking speeds. Since leg muscle reaction in the form of Electromyogram (EMG) signals and stride interval time series (as gait variability) have complex structures, we utilized fractal theory and sample entropy to decode their alterations at different walking speeds. Twenty-two subjects walked at three different speeds (slow, comfortable, and fast) in six trials, and we analyzed the fractal dimension and sample entropy of EMG signals and stride interval time series. Based on the results, increasing the walking speed causes lower complexity in EMG signals and stride interval time series. Besides, strong correlations were found among the changes in the complexity of EMG signals and stride interval time series at different walking speeds. This method can be applied to analyze the correlation between other complex physiological signals of humans (e.g., EEG and ECG) during walking and running.

Abstract

In this research, for the first time, we evaluated the correlation between the variations of leg muscle reaction and gait at different walking speeds. Since leg muscle reaction in the form of Electromyogram (EMG) signals and stride interval time series (as gait variability) have complex structures, we utilized fractal theory and sample entropy to decode their alterations at different walking speeds. Twenty-two subjects walked at three different speeds (slow, comfortable, and fast) in six trials, and we analyzed the fractal dimension and sample entropy of EMG signals and stride interval time series. Based on the results, increasing the walking speed causes lower complexity in EMG signals and stride interval time series. Besides, strong correlations were found among the changes in the complexity of EMG signals and stride interval time series at different walking speeds. This method can be applied to analyze the correlation between other complex physiological signals of humans (e.g., EEG and ECG) during walking and running.