## 6 Conclusions  
The primary purpose of this study was to investigate the impact of varying only one of the input parameters, i.e., tolerance window, \(r\), used to calculate ApEn and SampEn. We agree with previous research groups [38; 52; 61] who suggested that if \(r\) is multiplied by the standard deviation, then practitioners need to be aware that any factor that affects variation, e.g., data length, nonstationarity, spikes, and outliers, will likely affect the tolerance. Thus, researchers should try multiple \(r\) values, examine the relative consistency of the entropy values, and provide their findings in Supplementary Material. However, since we have shown that ApEn and SampEn values were consistent across several values of \(r\), we are confident that the algorithms \(r=0.20\), \(r=2.5\), or \(r=0.30\)\(\times\) SD may be equally suitable for characterizing the COP time-series excursions in the anterior-posterior and medial-lateral excursions for bipedal feet together and tandem quiet standing postures. Ours is the first study to examine the non-linear dynamics of the center of pressure for tandem standing, a test we believe might be particularly useful for testing patients with neuropathology, e.g., athletes post-concussion. This recommendation is based on the good relative consistency of the entropy values that we reported with changing tolerance windows, and the ability of both ApEn and SampEn to distinguish more from less stable quiet standing postures. Our results demonstrated that although, on average, ApEn values were larger than SampEn values, one estimation technique was not particularly \"better\" than the other; that is, if used in longitudinal measures of COP time series either entropy algorithm would be acceptable. In other words, ApEn did not appear more biased, and both ApEn and SampEn exhibited relative consistency. Finally, based on the consistency of our results using a COP time series that was downsampled, we suggest that collecting ground reaction forces at 60 Hz would provide a sufficient data set for future laboratory and clinical testing. Finally, based on the consistency of our results using a COP time series that was downsampled, we suggest that collecting ground reaction forces at 60 Hz would provide a sufficient data set for future laboratory and clinical testing. Conceptualization, [PERSON], S.R. and G.A. ; Methodology, J.W., S.R. and G.A. ; Software, J.W. ; Validation, J.W. ; Formal Analysis, J.W. ; S.R., G.A. and D.W.Z. ; Resources, G.A. ; Data curation, J.W. and S.R. ; Writing (original draft)--J.W. ; Writing (review and editing). J.W., S.R., G.A. and D.W.Z. ; Visualization--J.W., S.R. and D.W.Z. ; Supervision, S.R., G.A. and D.W.Z. ; Project administration, S.R. and G.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted following the Declaration of Helsinki, and approved by the Institutional Review Board of Grand Valley State University (18-246-H and 9 March 2020) for studies involving humans. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data presented in this study are available on request from the corresponding author due to restrictions imposed by our institution on retired faculty. We acknowledge University administrative support for the use of equipment and materials needed for motion capture in the Biomechanics and Motor Performance Laboratory and all study participants. The authors declare no conflicts of interest.