Allometric Control of Human Gait

West, Bruce J.; Griffin, Lori A.

doi:10.1142/s0218348x98000122

Cited by 64 publications

(54 citation statements)

References 10 publications

(11 reference statements)

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“…The long-range correlations in stride dynamics are not accounted for by traditional central pattern generator models of rhythmic motor behavior (51,54). The observation that aging and neurologic disease diminish stride interval correlation properties parallels the effects of age and pathology on heartbeat dynamics described above, and may also generalize to modeling other neuroregulatory processes.…”

Section: Fractal Scaling In Other Signals: Human Gait Dynamicsmentioning

confidence: 88%

Fractal dynamics in physiology: Alterations with disease and aging

Goldberger

Amaral

Hausdorff

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

1,765

1,384

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According to classical concepts of physiologic control, healthy systems are self-regulated to reduce variability and maintain physiologic constancy. Contrary to the predictions of homeostasis, however, the output of a wide variety of systems, such as the normal human heartbeat, fluctuates in a complex manner, even under resting conditions. Scaling techniques adapted from statistical physics reveal the presence of long-range, power-law correlations, as part of multifractal cascades operating over a wide range of time scales. These scaling properties suggest that the nonlinear regulatory systems are operating far from equilibrium, and that maintaining constancy is not the goal of physiologic control. In contrast, for subjects at high risk of sudden death (including those with heart failure), fractal organization, along with certain nonlinear interactions, breaks down. Application of fractal analysis may provide new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as to monitoring the aging process. Similar approaches show promise in assessing other regulatory systems, such as human gait control in health and disease. Elucidating the fractal and nonlinear mechanisms involved in physiologic control and complex signaling networks is emerging as a major challenge in the postgenomic era.A hallmark of physiologic systems is their extraordinary complexity. The nonstationarity and nonlinearity of signals ( Fig. 1) generated by living organisms defy traditional mechanistic approaches based on homeostasis and conventional biostatistical methodologies. Recognition that physiologic time series contain ''hidden information'' has fueled growing interest in applying concepts and techniques from statistical physics, including chaos theory, to a wide range of biomedical problems from molecular to organismic levels (1, 2).This presentation describes one area of investigation that has engaged our collaborative attention, namely, fractal analysis of physiologic time series in health and disease. The discussion will focus primarily on certain features of the human heartbeat, one important example of complex physiologic fluctuations. The dynamics of another physiologic control system-human gait-is also briefly discussed. Recognizing that this topic represents only one selected aspect of the broad and rapidly expanding applications of complexity theory to biomedicine (Table 1), readers are referred to a number of useful reviews and references therein (1, 3-10).A motivating problem for our work is depicted in Fig. 1, which presents a dynamical self-test. Shown are 30-min heart rate time series from four subjects. Only one is from a healthy individual; the other three are from patients with life-threatening forms of heart disease. The problem is to identify the normal record. The (perhaps nonintuitive) answer to this ''test'' is given in the figure caption. Beyond its obvious diagnostic import, the problem of classifying temporal assays of integrated cardiac physiology has implications for understanding a...

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Section: Fractal Scaling In Other Signals: Human Gait Dynamicsmentioning

confidence: 88%

Fractal dynamics in physiology: Alterations with disease and aging

Goldberger

Amaral

Hausdorff

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

1,765

1,384

View full text Add to dashboard Cite

show abstract

“…Long-range correlations have been observed in the stride intervals of human walking [1,32,33] and these correlations change with various experimental interventions [2] and/or disease processes affecting the central nervous system [4,5]. Previous modeling efforts conducted to explain these experimental results have focused on properties of complex supra-spinal neural control mechanisms [1,7,8].…”

Section: Discussionmentioning

confidence: 99%

Possible biomechanical origins of the long-range correlations in stride intervals of walking

Gates

Dingwell

2007

Physica A: Statistical Mechanics and its Applications

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When humans walk, the time duration of each stride varies from one stride to the next. These temporal fluctuations exhibit long-range correlations. It has been suggested that these correlations stem from higher nervous system centers in the brain that control gait cycle timing. Existing proposed models of this phenomenon have focused on neurophysiological mechanisms that might give rise to these long-range correlations, and generally ignored potential alternative mechanical explanations. We hypothesized that a simple mechanical system could also generate similar long-range correlations in stride times. We modified a very simple passive dynamic model of bipedal walking to incorporate forward propulsion through an impulsive force applied to the trailing leg at each push-off. Push-off forces were varied from step to step by incorporating both "sensory" and "motor" noise terms that were regulated by a simple proportional feedback controller. We generated 400 simulations of walking, with different combinations of sensory noise, motor noise, and feedback gain. The stride time data from each simulation were analyzed using detrended fluctuation analysis to compute a scaling exponent, a. This exponent quantified how each stride interval was correlated with previous and subsequent stride intervals over different time scales. For different variations of the noise terms and feedback gain, we obtained short-range correlations (α < 0.5), uncorrelated time series (α = 0.5), long-range correlations (0.5 < α < 1.0), or Brownian motion (α > 1.0). Our results indicate that a simple biomechanical model of walking can generate long-range correlations and thus perhaps these correlations are not a complex result of higher level neuronal control, as has been previously suggested.

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“…This random variability has been shown [1,3,4,6,12] to exhibit long-time correlations, and suggested that the phenomenon of walking is a self-similar, fractal, activity. The existence of fractal time series better suggests that the nonlinear oscillators needed to model locomotion operates in the unstable, that is, in the chaotic regime.…”

Section: Central Pattern Generator and Locomotionmentioning

confidence: 91%

Nonlinear dynamical model of human gait

2003

Self Cite

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We present a nonlinear stochastic model of the human gait control system in a variety of gait regimes. The stride interval time series in normal human gait is characterized by slightly multifractal fluctuations. The fractal nature of the fluctuations become more pronounced under both an increase and decrease in the average gait. Moreover, the long-range memory in these fluctuations is lost when the gait is keyed on a metronome. The human locomotion is controlled by a network of neurons capable of producing a correlated syncopated output. The central nervous system is coupled to the motocontrol system, and together they control the locomotion of the gait cycle itself. The metronomic gait is simulated by a forced nonlinear oscillator with a periodic external force associated with the conscious act of walking in a particular way.

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Allometric Control of Human Gait

Cited by 64 publications

References 10 publications

Fractal dynamics in physiology: Alterations with disease and aging

Fractal dynamics in physiology: Alterations with disease and aging

Possible biomechanical origins of the long-range correlations in stride intervals of walking

Nonlinear dynamical model of human gait

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