## 4 Discussion
[41] noted that downsampling from 100 Hz to 50 Hz and 25 Hz produced a data set that appeared to be linearly less regular, i.e., increasing SampEn. They cautioned that researchers must identify how much change is driven by the neuromotor system and how much is a function of the data processing technique. [PERSON] et al. [12] noted that since postural sway typically lies between 0.15 and 0.4 Hz (and as high as 3 Hz), a sampling rate of 25 Hz should be sufficient to detect time-series patterns and better reflect the underlying postural sway pattern. Thus, they were interested in evaluating how sample entropy of COP time-series data sampled at 100 Hz (\(N\) = 2000) from prolonged standing tasks on normal and compliant surfaces would be affected by downsampling by two, three, and four. They found that although downsampling increased SampEn values, it had an insignificant effect on the comparisons to the original data sets. However, they concluded that if other researchers performed such procedures, they should be well justified. [PERSON] et al. [17] recommended, as best practice, that practitioners not exceed sampling data beyond 1000 Hz. We found that the COP waveforms were observationally nearly identical when comparing unfiltered data to the downsampled data and that the entropy values that were subsequently determined, i.e., average ApEn and SampEn, ranged from 0.08 to 0.90; values that are consistent with other published works. Incorrect parameter selection, i.e., vector length, \(m\), tolerance or threshold window, \(r\), and data length, \(N\), regardless of the biological time series being considered, can under mine the ApEn and SampEn discrimination capacity [54]. For this study, the embedding dimension, \(m=2\), and data set length, \(N\), were fixed input parameters. We focused on assessing entropy outcomes relative to changes in the tolerance window since this parameter may have the greatest influence on determining ApEn or SampEn [38] and is considered one of the most difficult to select [16]. Selecting a tolerance window too small has been shown to limit the number of matches found and selecting too large a window could lead to too many matches found and increase the probability [38]. Many approaches to calculating \(r\) have been suggested, including utilizing the standard deviation (SD) of the whole time series [34; 52], the standard error of the entropy values [55], predefined tolerance levels [56; 57; 58], and using a heuristic stochastic model [54]. Most commonly, the tolerance window is calculated as \(r\) times the standard deviation of the time series [34]. However, many researchers have reported determining, and using, \(r=0.2\times\) SD, with the rationale that it was commonly used in previous research. [PERSON] et al. [38] recommended that researchers test multiple \(r\) values, and if using the default method, i.e., \(r\times\) SD, test the relative consistency of \(r=0.15\), \(0.25\), and \(0.30\times\) SD, but this was based on both theoretical and gait data [16; 17], whereas the present work examined quiet biped and tandem standing balance data. On the other hand, [PERSON] et al. [16] reported that as \(r\) increased, the value of SampEn decreased, which is similar to our results for both ApEn and SampEn during quiet biped and tandem standing. [PERSON] et al. [52] examined changing parameters \(m\), \(r\), and \(N\) on ApEn and SampEn values in a COP time series, as well as the ability of these entropy measures to discriminate between young and older adults (nonfallers and fallers). They reported significant three-way interactions between \(m\), \(r\), and \(N\) confirming the sensitivity of ApEn and SampEn to the input parameters. Although we examined changes in entropy over a range of \(r\) values, similar to [PERSON] et al., we constrained the embedding dimension to two; we did not study the interaction of multiple \(m\)'s and \(r\)'s. Our study results are limited because we did not examine the interaction between changing embedding dimensions and tolerance windows.