A new multi-attractor model for the human posture stability system aimed to follow self-organized dynamics

Abstract

Human postural stability is a complex nonlinear system, which naturally exhibits coexisting attractors and influenced by various factors. This system continuously requires to employ the self-organization mechanisms to maintain postural stability. This functionality is equivalent to switching the system dynamics among its attractors. The aim of our study is to follow the variations of the postural dynamics at different time intervals. The center-of-pressure (CoP) was recorded during 60s routine walk from twenty healthy young adult men with no evidence of neuromuscular system diseases. The experiment was repeated three times for each subject. We designed a map-based model with multiple attractors and defined two indicators to quantify the system dynamics at various time intervals. To model the system self-organization, we slid a window along the CoP time series. For each window, the parameters and the state variables of the model were set based on the proposed indicators (nonlinear local features). Tracking the behavioral patterns of the posture system is one of the prominent results of this research. The proposed model not only can follow the local (short-time interval) behavior of the system but also its global dynamics variation is like the experimental data based on the correlation dimension (CD). The CD reveals system dynamics in the long-time intervals and reflects the number of the effective system's degrees of freedom. The proposed methods can be used to quantify the variation of information in other biological systems.