[PERSON] and [PERSON] [26] then revised [PERSON] formula, which led to a more direct estimate of the KS entropy; a change that became the standard entropy measure referred to as the Eckmann-Ruelle (E-R) entropy formula. According to [1], the formulas used to measure the complexity of a system were developed to analyze chaotic systems but were less successful in characterizing limited, noisy, and stochastically derived time series. The KS worked well for real dynamic systems, but even a small amount of noise made those algorithms meaningless, with values tending to infinity. In addition, a finite correlation dimension value could not guarantee that the process under examination was deterministic. KS was developed to classify deterministic systems depending on the rates of generation of information by determining an entropy rate. However, the former entropy formalism was unsuitable for statistical calculations. Therefore, based on the ground-breaking work of [PERSON] and colleagues, later modified by others [23,26] [PERSON]'s extension of the E-R formulations resulted in a statistical version that could be used to characterize experimental data series. Alternatively, [PERSON] described the use of ApEn, which measures correlation, persistence, or regularity in a biological time series and could be used to compare systems with deterministic and stochastic components [1]. ApEn was not intended to be an approximation of the KS entropy but a statistical measure, or quantification, of the rate of regularity in a biological time series [1]. Theoretically, low ApEn values reflect a persistent, repetitive, and predictive system and high values reflect independence between data, a low number of repeated patterns, and unpredictability [29]. With biological data sets in general, but human movement in particular, quantifying levels of complexity has become important. For example, newer motor control theories, e.g., dynamical systems theory, do not consider variability in movement as an error [17]. Dynamical systems theory considers the complexity of movement patterns, i.e., variability, to be associated with system stability. ApEn and SampEn have been shown to demonstrate the state and changes in the complexity of various physiological signals related to seated postural control in individuals with chronic stroke [30], electrocardiograms [31], electroencephalograms (EEG) [32; 33], heart rate variability [34; 35], and neural respiratory time series [8]. In complex systems, e.g., cardiac, respiratory, somatosensory, etc., lower ApEn values reflect persistent, repetitive, and predictive systems, with patterns that repeat themselves throughout the series [6; 36]. So, it is more appropriate to use terms like probability, predictability, and regularity, when describing the nature of a measurable complex system. In summary, using ApEn and SampEn was not meant to analyze complex systems comprehensively but to statistically quantify the dynamics of time series related to complex systems [37]. Based on the work by [PERSON] [34; 36] that characterized complex physiological systems using entropic measures, biomechanists have measured the excursions of the center of mass (COM) and center of pressure (COP) during quiet standing; the COP is the point of application of the vertical ground reaction force vector and represents a weighted average of all the pressures over the surface area in contact with the ground. The interpretation of chaotic and/or irregular excursions in the anterior-posterior and medial-lateral directions has been noted as a sign of poor balance and deficient postural control [2]. Alternatively, it has been suggested that chaotic excursions of a complex system may be interpreted as a characteristic of a successful strategy to maintain balance.