###### Abstract
It is worth noting that these two reviews [6; 10] discussing the assessment of gait stability did not mention entropy, despite its recent popularity among nonlinear measures [12]. However, the papers mentioned focused on the possibility of predicting the probability of falling, and entropy does not have such predictive capabilities. Despite this, the use of entropy analysis in gait research has increased significantly over the past two decades [13]. Entropy analysis in gait research involves the application of principles from information theory to quantify the complexity, variability, and predictability within human gait patterns [14; 15]. The concept of entropy, derived from thermodynamics and information theory, measures the randomness or disorder in a system. In the context of gait analysis, entropy-based approaches aim to characterize the complexity and regularity of human movement [13; 15]. There are various entropy metrics used in gait research: approximate entropy (ApEn) [16], Shannon entropy [17], multiscale entropy (MSE) [17], and sample entropy (SampEn) [14; 18]. ApEn measures the regularity and complexity of a time-series signal. It quantifies the unpredictability of fluctuations in gait patterns. Lower ApEn values suggest greater regularity or predictability, whereas higher values indicate more irregularity. Shannon entropy measures the uncertainty of average information content within a system. In gait research, it can quantify the diversity or variability of movement patterns across different conditions or populations. MSE examines entropy across multiple temporal scales, capturing how complexity changes over different timescales within a gait signal. It provides insights into both short-term and long-term dynamics of gait patterns. Like ApEn, SampEn quantifies the complexity/regularity/predictability/probability of analyzed motion [12; 13]. The SampEn (\(m\), \(r\), \(N\)) of a dataset of length \(N\) measures the temporal pattern within the signal, assessing the logarithmic probability that two similar sequences having an identical number of data points (\(m\)) will continue to be similar when an additional data point is introduced (\(m+1\)) without allowing self-matches [19]. Thus, the SampEn algorithm necessitates three essential parameters: tolerance window \(r\) (used to determine whether patterns within the time series are similar or not), vector length \(m\) (data length compared across the time series to determine conditional probabilities), and time series length \(N\). For gait data analysis, \(m\) = 2, 4, 6, 8 or 10 is most often used, while \(r\) should be equal to 0.2 \(\times\) SD, where SD is the standard deviation of the analyzed time series [15; 18; 20; 21]. An important fact is that SampEn offers an advantage in its independence from data length. It considers self-matches (repeated patterns) in the signal and is less sensitive to variations in data length compared to ApEn [22]. However, Richman and Moorman [22] recommended caution with datasets smaller than 200 points. Naturally, such short time series are unlikely in gait analysis when dealing with multiple gait cycles. Lower values of SampEn indicate greater regularity or predictability, which is associated with less complexity of structure [23]. Higher values suggest more complexity or randomness in the data. Given that complexity plays a crucial role in adapting to an environment, reduced complexity in physical movement results in diminished flexibility and increased rigidity in postural control [24]. Conversely, higher SampEn values (suggesting increased complexity) are interpreted as increased self-organization and an effective strategy in retaining postural control [25]. Applications of entropy analysis in gait research include characterizing gait variability, assessing gait stability, distinguishing healthy and pathological gaits, and understanding gait adaptation.