Thus, even though the amount of variability is deeply related to the exploration of the individual to find a suitable solution, it does not inform about how movement behavior changes over time. It must be taken into account to fully understand the potential relationship between motor variability and the DOFs that the movement dynamics of the biological systems is nonlinear. Thus, the study of motor system variations has been an interesting topic for several researchers to study how variability changes according to the skill level or the learning stages and to search for the optimal level of variability to optimize the adaptation process. How does this work? Human motor variability would promote these adaptive behaviors by facilitating the exploration of the motor system redundancy and the ability to perform motor adjustments in order to refine movement performance during the interaction with changing environments. Different authors have backed the idea that motor variability gives the system the flexibility to choose the right DoFs to facilitate the adaptation to environmental changes. The aim of this interaction is to achieve a successful motor solution by choosing a suitable degree of freedom (DoF) configuration among the multiple and redundant DoFs depending on the performer’s level and environment inputs, i.e., to choose the functional DoFs (defined as the very limited DoF during specific movement tasks) from all the available mechanical DoFs (defined as the minimum number of independent coordinates needed to describe a system’s position). Human movements are the result of the complex interaction between all the independent elements that make up the human body and their relationship with the environment. The studies that analyze motor variability should consider the type of movement and the DoFs involved in the analyzed task since, as the findings have shown, both factors have a noticeable influence on the amount and the structure of motor variability. The results showed a higher amount of variability and a less predictable and more auto-correlated variability structure in the long-term when more mechanical DoFs are implied. For this purpose, movement variability was assessed by a tracking sensor in five tasks with different DoFs, and the amount, using standard deviation, and the structure of variability, through fuzzy entropy and detrended fluctuation analysis, were also assessed. The aim of this study was to analyze how motor variability, both the amount and structure, changes depending on the mechanical DoFs involved in the movement control. However, the potential relationship between motor variability and DoFs is unknown. It has been stated that motor variability is related to the system’s flexibility needed to choose the right degrees of freedom (DoFs) to adapt to constant environmental changes. Motor variability is a prominent feature of the human movement that, nowadays, can be easily measured through different sensors and analyzed using different types of variables, and it seems to be related to functional and adaptative motor behavior.
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