It is notable that during quiet standing, the displacements of the COP display highly irregular and non-stationary fluctuations [13] that are not just the result of white noise but display non-linear dynamics that consist of an orderliness (which may appear random) that emerge over time, likely the result of the complex interactions among underlying postural control systems, i.e., visual, vestibular, and somatosensory [10]. For example, under fixed tasks, e.g., quiet standing, and environmental conditions, the non-linear properties of the postural system likely arise partly due to elastic and damping characteristics of muscles [10]. Because typical biomechanical measures of postural stability, e.g., the magnitude of the mediolateral excursion of the COP, have been based on linear statistical models, it has been shown that they cannot detect subtle changes in postural control [18]. For example, some research found that, despite reporting concussion-related symptoms, many athletes continue to display postural instability [18]. The quantification of the non-linear dynamic nature of COP measures using entropic measures, such as ApEn and SampEn, has made it possible to sort out the issues of system complexity versus regularity. [PERSON] et al. [13] tested quiet standing posture under eyes-open and eyes-closed conditions in young healthy individuals. They reported a significant reduction in the SampEn in the COP anterior-posterior and medial-lateral excursion in the eyes closed condition. A reduction in ApEn or SampEn suggests a more regular time series, indicating that the postural control system was more constrained in a potentially less stable task. [PERSON] and [PERSON] [2] compared the SampEn of the COP time series in two dimensions from quiet standing between young and elderly adults. In that study, there was greater entropy in the anterior-posterior direction in the elderly group suggesting the effect of a more impaired sensory system, which provided less precise input for balance control. Motivated by retrospective data that illustrated that COP oscillations in the anterior-posterior and medial-lateral directions became more regular (lower ApEn value) after injury despite the absence of postural instability, [PERSON] et al. [18] reported that ApEn values for the AP and ML COP time series generally declined immediately after injury (i.e., concussion) in both steady (normal COP metrics) and unsteady injured athletes, suggesting a postural control system that was more constrained after injury. In that same study, reduced ApEn values were still evident 48 to 96 h after injury although postural instability had resolved. In a follow-up study, [PERSON] et al. [19] further substantiated the usefulness of ApEn by examining its responsiveness to evaluate the immediate, short-term effect of secondary cognitive task performance on postural control in healthy, young adults. During dual-task performance, ApEn revealed a change in the randomness of COP oscillations in various sensory conditions, illustrating the potential of ApEn to detect subtle changes in postural control. Based on this review, it seems clear that both ApEn and SampEn methods have the potential to assist in the clarification of neuropathology, as well as their medical management. [PERSON] considered ApEn (\(m\), \(r\)) as a family of formulas and ApEn (\(m\), \(r\), \(N\)) as a family of statistics and suggested that system comparisons are possible with fixed \(m\) and \(r\)[34]. Yet both ApEn and SampEn methods utilize four input parameters: (1) \(N\) is the data length, (2) \(m\) is the pattern length (often referred to as the embedding dimension, vector length, segment length, or pattern window), (3) \(r\) is the tolerance window (a de facto noise filter), and (4) the delay used in the embedding. ApEn and SampEn quantify complexity in the data by looking at the difference in \(m\) point vs. \(m+1\) point patterns, over the \(N\) point data length.