###### Abstract
The latter aspect was overcome by more recently introduced entropy measures, wherein samples' distribution patterns are considered [28; 29]; in particular, the permutation entropy (PEn) and its modifications rely on an ordinal mapping of the neighboring values [30; 31], evaluating their relative occurrence and avoiding the use of distance metrics. Despite the use of the PEn for time-series analysis representing a still investigated issue [32], it has been successfully applied on EEG data for detecting epileptic seizures [31; 33; 34]. Further, PEn was also used in ECG data analysis in order to investigate behavioral states [35] and for the analysis of heart rate variability [36; 37]. However, the validity of using a complexity measure as the PEn cannot be blindly extended to other biological signals, different to those listed above, such as the sEMG ones, due to their different characteristics [1]; bear in mind also that it relies on ordinal mapping of data samples, thereby avoiding amplitude information which otherwise appears worth considering for some applications [31]. Thus, as for the FEn, the investigation of the PEn as a possible feature for sEMG-driven gesture recognition appears to be yet lacking. As happens for all the entropy metrics, FEn and PEn rely on the a priori determination of unknown parameters [16; 32], and therefore a fundamental aspect which deserves to be carefully considered when dealing with complexity measures in time-series analysis is the influence of computational parameters. The FEn depends upon the embedding dimension \(m\), which represents the length of the subseries to be compared, and on other two parameters, which account for acknowledging or denying the similarity between subseries (see Section 2). Generally speaking, the parameters related to the latter aspect play a crucial role in obtaining a reliable entropy computation and their role has been investigated for a number of different entropy algorithms [38], and for biological signal analysis [39; 40]. On the other hand, the PEn computation is based on an ordinal mapping of the embedding vectors (see Section 2) and thus the choice of the embedding dimension \(m\) becomes crucial for obtaining a reliable estimation of complexity [32]. The goal of this study was to investigate the possible applicability of both FEn and PEn as significant features for distinguishing different hand gestures in healthy subjects, through the characterization of the sEMG signal acquired by a low-cost device such as the Myo armband. The major contributions provided by this work can be summarized as follows: Firstly, two complexity measures, based on highly different computational core logics, have been investigated as possible features for hand gesture characterization. The FEn was originally presented and evaluated for characterizing myoelectric signals for hand gesture recognition, but it was not compared with other different time or frequency domain features. On the other hand, despite it having been used for analyzing biological signals, the PEn has not been exploited for analyzing EMG time series, and thus its suitability as a possible feature able to adequately describe different gestures from sEMGrecordings has not been investigated. Secondly, the choice of computational parameters represents a crucial aspect when dealing with complexity measures for time-series analysis. Therefore, for both FEn and PEn the influences of computational parameters were examined with respect to their gesture clustering capabilities, while providing a possible set of optimal parameters for each considered set of hand movements. Thirdly, the suitability of both FEn and PEn for hand gesture characterization was evaluated in three different ways. As reported above, the computational parameters were selected while relying on the best clustering of hand gestures.