Format

Send to

Choose Destination
PeerJ. 2019 Aug 1;7:e7417. doi: 10.7717/peerj.7417. eCollection 2019.

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

Terrier P1,2,3.

Author information

1
Haute Ecole Arc Santé, HES-SO University of Applied Sciences and Arts Western Switzerland, Neuchâtel, Switzerland.
2
Clinique romande de réadaptation SUVA, Sion, Switzerland.
3
Department of Thoracic and Endocrine Surgery, University Hospitals of Geneva, Geneva, Switzerland.

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.

KEYWORDS:

Center of pressure; Complexity; Divergence exponent; Gait variability; Human locomotion; Instrumented treadmill; Metronome walking; Nonlinear analysis; Sensorimotor synchronization

Supplemental Content

Full text links

Icon for PeerJ, Inc. Icon for PubMed Central
Loading ...
Support Center