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Background: Human gait exhibits complex fractal fluctuations amongconsecutive strides. The time series of gait parameters are long-rangecorrelated (statistical persistence). In contrast, when gait is synchronizedwith external rhythmic cues, the fluctuation regime is modified to stochasticoscillations around the target frequency (statistical anti-persistence). Tohighlight these two fluctuation modes, the prevalent methodology is thedetrended fluctuation analysis (DFA). The DFA outcome is the scaling exponent,which lies between 0.5 and 1 if the time series exhibit long-rangecorrelations, and below 0.5 if the time series is anti-correlated. Afundamental assumption for applying DFA is that the analyzed time seriesresults from a time-invariant generating process. However, a gait time seriesmay be constituted by an ensemble of sub-segments with distinct fluctuationregimes (e.g., correlated and anti-correlated). Methods: Several proportions ofcorrelated and anti-correlated time series were mixed together and thenanalyzed through DFA. The original (before mixing) time series were generatedvia autoregressive fractionally integrated moving average (ARFIMA) modelling oractual gait data. Results: Results evidenced a nonlinear sensitivity of DFA tothe mix of correlated and anti-correlated series. Notably, adding a smallproportion of correlated segments into an anti-correlated time series hadstronger effects than the reverse. Significance: In case of changes in gaitcontrol during a walking trial, the resulting time series may be a patchyensemble of several fluctuation regimes. When applying DFA, the scalingexponent may be misinterpreted. Cued walking studies may be most at risk ofsuffering this issue in cases of sporadic synchronization with external cues.

Gait complexity assessed by detrended fluctuation analysis is sensitive to inconsistencies in stride time series: A modeling study