Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues

Abstract

Background During steady walking, gait parameters fluctuate from one stride to another with complex fractal patterns and long-range statistical persistence. When a metronome is used to pace the gait (sensorimotor synchronization), long-range persistence is replaced by stochastic oscillations (anti-persistence). Fractal patterns present in gait fluctuations are most often analyzed using detrended fluctuation analysis (DFA). This method requires the use of a discrete times series, such as intervals between consecutive heel strikes, as an input. Recently, a new nonlinear method, the attractor complexity index (ACI), has been shown to respond to complexity changes like DFA, while being computed from continuous signals without preliminary discretization. Its use would facilitate complexity analysis from a larger variety of gait measures, such as body accelerations. The aim of this study was to further compare DFA and ACI in a treadmill experiment that induced complexity changes through sensorimotor synchronization. Methods Thirty-six healthy adults walked 30 min on an instrumented treadmill under three conditions: no cueing, auditory cueing (metronome walking), and visual cueing (stepping stones). The center-of-pressure trajectory was discretized into time series of gait parameters, after which a complexity index (scaling exponent alpha) was computed via DFA. Continuous pressure position signals were used to compute the ACI. Correlations between ACI and DFA were then analyzed. The predictive ability of DFA and ACI to differentiate between cueing and no-cueing conditions was assessed using regularized logistic regressions and areas under the receiver operating characteristic curves (AUC). Results DFA and ACI were both significantly different among the cueing conditions. DFA and ACI were correlated (Pearson’s r = 0.86). Logistic regressions showed that DFA and ACI could differentiate between cueing/no cueing conditions with a high degree of confidence (AUC = 1.00 and 0.97, respectively). Conclusion Both DFA and ACI responded similarly to changes in cueing conditions and had comparable predictive power. This support the assumption that ACI could be used instead of DFA to assess the long-range complexity of continuous gait signals. However, future studies are needed to investigate the theoretical relationship between DFA and ACI.

Comparing the effects of metronome walking on long-term attractor divergence of gait dynamics and on correlation structure of stride intervals: a validation study in older people

“…Previous treadmill studies 36,37,61 have suggested this relationship, bur our investigation is the rst to empirically validate it in a large cohort of older adults engaged in normal (overground) walking. Disentangling the relationships between these variables is a complex task.…”

“…We suggested that, when gait is synchronized to a metronome, there is a shift to oscillations around a set target value, and thus the longterm divergence is damped more quickly within a narrowly bounded attractor. We 22,36,37,60 , and others 61,62 , have repeatedly found that scaling exponents and long-term attractor divergent exponents (DE) are correlated and respond similarly to metronome walking. A more recent study further elucidated the sensitivity of long-term divergence to changes in the correlation structure between strides 36 .…”

“…The SDD represents the smallest genuine change that can be detected, considering the variability of the measure across several trials 84,88 . Sample size.Previous treadmill studies have reported standardized effect sizes greater than 2.0 in younger individuals when comparing metronome walking to standard walking conditions using long-term DE (ACI) as a measure 22,37 . Based on this effect size, with an alpha level of α=0.01 and a power of 1-β=0.9, the expected minimum sample size was determined to be 8 subjects.…”

Gauging Gait Disorders with a Method Inspired by Motor Control Theories: A Pilot Study in Friedreich’s Ataxia

“…It is not only unnecessary and harmful noise, but a noise that participates in an adaptive function [ 26 ] and has the potential to be a source of information on the different states available. Regarding human ambulation, multiple studies have demonstrated that the complex fluctuations in stride intervals, stride speeds, and stride lengths exhibit fractal patterns with inverse power-law memory [ 27 , 28 ]; that is, a change occurring at a given gait cycle can potentially influence another cycle dozens of steps later [ 29 ]. For clinics, the main interest of gait dynamics lies in providing information about whether a gait is highly controlled or more automated [ 29 ].…”

“…Regarding human ambulation, multiple studies have demonstrated that the complex fluctuations in stride intervals, stride speeds, and stride lengths exhibit fractal patterns with inverse power-law memory [ 27 , 28 ]; that is, a change occurring at a given gait cycle can potentially influence another cycle dozens of steps later [ 29 ]. For clinics, the main interest of gait dynamics lies in providing information about whether a gait is highly controlled or more automated [ 29 ]. Therefore, it opens the door to understanding what is due to perturbation (primary disease) or regulation (adaptative processes).…”