This study proposes a generalization of the ankle and hip postural strategies to be applied to the large class of skills that share the same basic challenge of defeating the destabilizing effect of gravity on the basis of the same neuromotor control organization, adapted and specialized to variable number of degrees of freedom, different body parts, different muscles, and different sensory feedback channels. In all the cases we can identify two crucial elements (the CoP - Center of Pressure and the CoM - Center of Mass) and the central point of the paper is that most balancing skills can be framed in the CoP-CoM interplay and can be modeled as a combination/alternation of two basic stabilization strategies: the standard well investigated COPS (or CoP strategy, the default option), where the CoM is the controlled variable and the CoP is the control variable, and the less investigated COMS (or CoM strategy), where CoP and CoM must exchange their role because the range of motion of the CoP is strongly constrained by environmental conditions.

The paper focuses on the tightrope balancing skill where sway control in the sagittal plane is modeled in terms of the COPS while the more challenging sway in the coronal plane is modeled in terms of the COMS, with the support of a suitable balance pole. Both stabilization strategies are implemented as state-space intermittent, delayed feedback controllers, independent of each other. Extensive simulations support the degree of plausibility, generality, and robustness of the proposed approach.

This is a Matlab Simulation Script, named tightrope.m, that was used for generating the figures and for computing the numerical results discusses in the paper.

The user needs Matlab R2017a or later version. He/she must import the script in the current folder. The it is sufficient to type the name of the script in the command window and the results will appear in the workspace. Please note that each simulation run produces slightly different results because the control variables are affected by white noise that is refreshed for each run.