Coordination dynamics of (a)symmetrically loaded gait

Daniel M. Russell, Joshua L. Haworth, Cesar Martinez-Garza

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Asymmetries in the resonant frequency of limbs/effectors lead to changes in coordination dynamics, including deviations in relative phase at ϕ = 0 or π rad and reduced stability. These effects have been successfully modeled by the extended Haken–Kelso–Bunz (HKB) coupled oscillator model (Kelso et al. in Attention and performance XIII. Erlbaum, Hillsdale, pp 139–169, 1990), and supported in laboratory tasks of rhythmic limb motions. Efforts to apply the HKB model to walking have supported the predicted deviations in phase, but not the expected decreases in coordination stability. The lack of stability effects arising from asymmetries may be due to the stabilizing influence of a treadmill or may be obscured by the balance requirements and ground impacts in gait. This study examined these possibilities by investigating walking overground with ankle weights of 3 or 6 kg to create asymmetries between the legs, as well as symmetrical loads. Participants walked without a metronome and separately with a metronome to control speed and cadence. Coordination dynamics between the legs were quantified through mean and standard deviation (SD) of ϕ, while individual leg local dynamic stability was calculated as maximum Lyapunov exponent (λMAX). Irrespective of the condition, asymmetrical loads led to deviations in phase from antiphase with the loaded leg lagging behind the other, and both SDϕ and λMAX increased (i.e., stability decreased). Symmetrical loads had no effect on phase deviations, but decreased stability. Overall, these findings indicate that the HKB model captures coordination dynamics in walking, but also highlights limitations in modeling the influence of loads on an individual limb.

Original languageEnglish (US)
Pages (from-to)867-881
Number of pages15
JournalExperimental Brain Research
Issue number3
StatePublished - Mar 1 2016

All Science Journal Classification (ASJC) codes

  • General Neuroscience


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