Abstract
Diverse indicators of postural control in Humans have been explored for decades, mostly based on the trajectory of the center-of-pressure. Classical approaches focus on variability, based on the notion that if a posture is too variable, the subject is not stable. Going deeper, an improved understanding of underlying physiology has been gained from studying variability in different frequency ranges, pointing to specific short-loops (proprioception), and long-loops (visuo-vestibular) in neural control. More recently, fractal analyses have proliferated and become useful additional metrics of postural control. They allowed identifying two scaling phenomena, respectively in short and long timescales. Here, we show that one of the most widely used methods for fractal analysis, Detrended Fluctuation Analysis, could be enhanced to account for scalings on specific frequency ranges. By computing and filtering a bank of synthetic fractal signals, we established how scaling analysis can be focused on specific frequency components. We called the obtained method Frequency-specific Fractal Analysis (FsFA) and used it to associate the two scaling phenomena of postural control to proprioceptive-based control loop and visuo-vestibular based control loop. After that, convincing arguments of method validity came from an application on the study of unaltered vs. altered postural control in athletes. Overall, the analysis suggests that at least two timescales contribute to postural control: a velocity-based control in short timescales relying on proprioceptive sensors, and a position-based control in longer timescales with visuo-vestibular sensors, which is a brand-new vision of postural control. Frequency-specific scaling exponents are promising markers of control strategies in Humans.
Highlights
The regulation of human posture is operated via several feedback loops compensating internal and external disturbances (Diener et al, 1984)
It appears clearly that the filtering of synthetic fractional Gaussian Noise (fGn) signals generated a crossover in the log(F(n)) vs. log(n) relationship established with Detrended Fluctuation Analysis (DFA)
We hypothesized that scaling analysis of CoP dynamics may benefit from taking into account physiological backgrounds classically associated with the frequency components of the CoP signal, to uncover postural control strategies
Summary
The regulation of human posture is operated via several feedback loops compensating internal and external disturbances (Diener et al, 1984). Study of the postural sway has shown to be a valuable indicator to assess the quality and characteristics of the regulation of posture It was used among other applications for assessment of frailty in elderly people (Jiang et al, 2013; Zhou et al, 2017), to predict falls in chronic stroke survivors (Punt et al, 2017), in the identification of idiopathic scoliosis (Gruber et al, 2011), in differentiating postural strategies. FsFA for Postural Control Strategies in athletes and non-athletes (Paillard et al, 2002; Lamoth and van Heuvelen, 2012), to analyze the effects of a dual task (Kang et al, 2009), or of fear of falling (Davis et al, 2009) on posture Most importantly, it has become an indicator of efficiency for rehabilitation procedures (Priplata et al, 2002; Costa et al, 2005; Chen and Jiang, 2014; Wayne et al, 2014). Projection allows classical methods for the analysis of one-dimensional time-series to be used
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