A Stochastic Model of Human Gait Dynamics
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TLDR
In this article, a stochastic model of gait rhythm dynamics, based on transitions between different "neural centers" that reproduces distinctive statistical properties of normal human walking is presented.Abstract:
We present a stochastic model of gait rhythm dynamics, based on transitions between different “neural centers”, that reproduces distinctive statistical properties of normal human walking. By tuning one model parameter, the transition (hopping) range, the model can describe alterations in gait dynamics from childhood to adulthood—including a decrease in the correlation and volatility exponents with maturation. The model also generates time series with multifractal spectra whose broadness depends only on this parameter. Moreover, we find that the volatility exponent increases monotonically as a function of the width of the multifractal spectrum, suggesting the possibility of a change in multifractality with maturation.read more
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Gait variability: methods, modeling and meaning
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Gait dynamics, fractals and falls: finding meaning in the stride-to-stride fluctuations of human walking.
TL;DR: Support for the idea that gait dynamics has meaning has meaning and may be useful in providing insight into the neural control of locomotion and for enhancing functional assessment of aging, chronic disease, and their impact on mobility is discussed.
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Gait dynamics in Parkinson’s disease: Common and distinct behavior among stride length, gait variability, and fractal-like scaling
TL;DR: This update highlights the idea that while stride length, gait variability, and fractal scaling of gait are all impaired in PD, distinct mechanisms likely contribute to and are responsible for the regulation of these disparate gait properties.
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Self-organized criticality model for brain plasticity
TL;DR: A model that is based on self-organized criticality and takes into account brain plasticity, which is able to reproduce the spectrum of electroencephalograms (EEG) and indicates that universality holds for a wide class of brain models.
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