ArticlePeerJ2019
Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 14 citations in OpenAlex.
- Beyond gait speed: a multidimensional motor signature of motoric cognitive risk syndrome identified through domain-specific anomaly detection.GeroScience · 2026Article
- Validity of Linear and Nonlinear Measures of Gait Variability to Characterize Aging Gait with a Single Lower Back Accelerometer.Sensors (Basel, Switzerland) · 2024Article
- Effects of metronome walking on long-term attractor divergence and correlation structure of gait: a validation study in older people.Scientific reports · 2024Article
- Application of vibration to the soles increases long-range correlations in the stride parameters during walking.Heliyon · 2023Article
- Gauging Gait Disorders with a Method Inspired by Motor Control Theories: A Pilot Study in Friedreich's Ataxia.Sensors (Basel, Switzerland) · 2021Article
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1 author at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDuring 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.
methodsThirty-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).
resultsDFA and ACI were both significantly different among the cueing conditions. DFA and ACI were correlated (Pearson's
conclusionBoth 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.
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