scholarly journals ANALYSIS OF THE CORRELATION BETWEEN MUSCLE REACTION AND STRIDE INTERVAL VARIABILITY IN SINGLE-TASK AND DUAL-TASK WALKING

Fractals ◽  
2021 ◽  
Author(s):  
NORAZRYANA MAT DAWI ◽  
BALAMURALI RAMAKRISHNAN ◽  
FILIP MALY ◽  
KAMIL KUCA ◽  
HAMIDREZA NAMAZI
Keyword(s):  
Time Series ◽  
Dual Task ◽  
Muscle Activation ◽  
Approximate Entropy ◽  
Gait Variability ◽  
Single Task ◽  
Interval Time ◽  
Stride Interval ◽  
Interval Time Series ◽  
Leg Muscle

Analysis of leg muscle activation and gait variability during locomotion is an important area of research in physiological and sport sciences. In this paper, we analyzed the coupling between the alterations of leg muscle activation and gait variability in single-task and dual-task walking. Since leg muscle activation in the form of electromyogram (EMG) signals and gait variability in the form of stride interval time series have complex structures, fractal theory and approximate entropy were used to evaluate their correlation at various walking conditions. Sixty subjects walked at their preferred speed for 10 min under the single-task condition and for 90[Formula: see text]s under the cognitive dual-task condition, and we evaluated the variations of the fractal dimension and approximate entropy of EMG signals and stride interval time series. According to the results, dual-task walking caused reductions in the complexity of EMG signals and stride interval time series than single-task walking. This technique can be used to evaluate the correlation between other organs during different locomotion.

scholarly journals Added Value and Clinical Significance of Nonlinear Variability Indices of Walking Stride Interval in Neurodegenerative Diseases

2020 ◽  
Author(s):  
Dierick Frédéric ◽  
Vandevoorde Charlotte ◽  
Chantraine Frédéric ◽  
White Olivier ◽  
Buisseret Fabien
Keyword(s):  
Time Series ◽  
Neurodegenerative Diseases ◽  
Clinical Significance ◽  
Aging Process ◽  
Gait Pattern ◽  
Added Value ◽  
Series Length ◽  
Interval Time ◽  
Stride Interval ◽  
Interval Time Series

AbstractThough self-paced walking is highly stereotyped, the stride interval fluctuates from one stride to the next around an average value with a measurable statistical variability. In clinical gait analysis, this variability is usually assessed with indices such the standard deviation or the coefficient of variation (CV). The aim of this study is to understand the added value that nonlinear indices of walking stride interval variability, such as Hurst exponent (H) and Minkowski fractal dimension (D), can provide in a clinical context and to suggest a clinical significance of these indices in the most common neurodegenerative diseases: Parkinson, Huntington, and amyotrophic lateral sclerosis. Although evidence have been accumulated that the stride interval organization at long range displays a more random, less autocorrelated, gait pattern in neurodegenerative diseases compared with young healthy individuals, it is today necessary to recompute CV, H, and D indices in a unified way and to take into account aging impact on these indices. In fact, computed nonlinear indices of variability are mainly dependent on stride interval time series length and algorithms used, making quantitative comparisons between different studies difficult or even impossible. Here, we recompute these indices from available stride interval time series, either coming from our lab or from online databases, or made available to us by the authors of previously conducted research. We confirm that both linear and nonlinear variability indices are relevant indicators of aging process and neurodegenerative diseases. CV is sensitive to aging process and pathology but does not allow to discriminate between specific neurodegenerative diseases. D shows no significative change in pathological groups. However, since H index is correlated with Hoehn & Yahr scores and significantly lowered in patients suffering from Huntington’s disease, we recommend it as a relevant supplement to CV.

Prediction of stride interval time series

2016 ◽  
Author(s):  
Etienne Zahnd ◽  
Jennifer S. Brack ◽  
Subashan Perera ◽  
Ervin Sejdic
Keyword(s):  
Time Series ◽  
Interval Time ◽  
Stride Interval ◽  
Interval Time Series

Allometric Control of Human Gait

Fractals ◽  
1998 ◽  
Vol 06 (02) ◽  
pp. 101-108 ◽  
Author(s):  
Bruce J. West ◽  
Lori Griffin
Keyword(s):  
Time Series ◽  
Power Law ◽  
Relative Dispersion ◽  
Human Gait ◽  
Interval Time ◽  
Inverse Power Law ◽  
Time Correlations ◽  
Stride Interval ◽  
Interval Time Series ◽  
Long Time

The stride interval in normal human gait is not strictly constant, but fluctuates from step to step in a random manner. These fluctuations have traditionally been assumed to be uncorrelated random errors with normal statistics. Herein we show that, contrary to thes assumption these fluctuations have long-time correlations. Further, these long-time correlations are interpreted in terms of a scaling in the fluctuations indicating an allometric control process. To establish this result we measured the stride interval of a group of five healthy men and women as they walked for 5 to 15 minutes at their usual pace. From these time series we calculate the relative dispersion, the ratio of the standard deviation to the mean, and show by systematically aggregating the data that the correlation in the stride-interval time series is an inverse power law similar to the allometric relations in biology. The inverse power-law relative dispersion shows that the stride-interval time series scales indicating long-time self-similar correlations extending for hundreds of steps, which is to say that the underlying process is a random fractal. Furthermore, the power-law index is related to the fractal dimension of the time series. To determine if walking is a nonlinear process the stride-interval time series were randomly shuffled and the differences in the fractal dimensions of the surrogate time series from those of the original time series were determined to be statistically significant. This difference indicates the importance of the long-time correlations in walking.

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