Dataset Information

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.

SUBMITTER: Terrier P

PROVIDER: S-EPMC6679905 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Terrier Philippe P

PeerJ 20190801

<h4>Background</h4>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 ...[more]

PMID: 31396452

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Project description:Typically, gait rehabilitation uses an invariant stimulus paradigm to improve gait related deficiencies. However, this approach may not be optimal as it does not incorporate gait complexity, or in more precise words, the variable fractal-like nature found in the gait fluctuations commonly observed in healthy populations. Aging which also affects gait complexity, resulting in a loss of adaptability to the surrounding environment, could benefit from gait rehabilitation that incorporates a variable fractal-like stimulus paradigm. Therefore, the present study aimed to investigate the effect of a variable fractal-like visual stimulus on the stride-to-stride fluctuations of older adults during overground walking. Additionally, our study aimed to investigate potential retention effects by instructing the participants to continue walking after turning off the stimulus. Older adults walked 8 min with i) no stimulus (self-paced), ii) a variable fractal-like visual stimulus and iii) an invariant visual stimulus. In the two visual stimuli conditions, the participants walked 8 additional minutes after the stimulus was turned off. Gait complexity was evaluated with the widely used fractal scaling exponent calculated through the detrended fluctuation analysis of the stride time intervals. We found a significant ~20% increase in the scaling exponent from the no stimulus to the variable fractal-like stimulus condition. However, no differences were found when the older adults walked to the invariant stimulus. The observed increase was towards the values found in the past to characterize healthy young adults. We have also observed that these positive effects were retained even when the stimulus was turned off for the fractal condition, practically, acutely restoring gait complexity of older adults. These very promising results should motivate researchers and clinicians to perform clinical trials in order to investigate the potential of visual variable fractal-like stimulus for gait rehabilitation.

Dataset Information

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

Publications

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

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