###### Abstract
Throughout the years, many different features and sets of features have been proposed, and their suitability for accurate gesture classification has been extensively investigated. Myoelectric features can be roughly sorted into three main categories: time domain features, frequency domain features and time-frequency domain features [7]. [PERSON] et al. [21] proposed a set of time domain features, encompassing mean absolute value, waveform length, slope sign change and zero crossing, which was later modified by [PERSON] et al. [22] by adding the integrated EMG measure, the variance and the Willison amplitude. In some cases, time domain measures were also considered in conjunction with autoregressive model coefficients [19,23]. For what concerns the frequency-domain features, a number of different measures are considered based on the power spectral density (PSD) of the EMG signal. The most used ones can be recognized to be the mean and median frequency and the mean and total power spectrum [7,8,17]. Additional commonly used features are the peak frequency and the frequency ratio [9]. Nonetheless, the reliability of frequency domain features represent a still debated issue [4] and their effectiveness for hand gesture recognition was not always acknowledged, providing at most the same discrimination of the features in time domain, without further advantages [9]. On the other hand, time-frequency domain features, e.g., discrete wavelet coefficients, discrete wavelet packet coefficients and continuous wavelet transform coefficients, e.g., [24,25], demonstrated little improvements in myoelectric pattern recognition with respect to time domain ones [4], which take advantage by being relatively simple to calculate, with a low computational load [4]. In addition, [PERSON] et al. [9] investigated a set of thirty-seven time and frequency domain measures, showing that considerable levels of accuracy in gesture recognition can be attained by using also a very small number of time domain features. Therefore, time domain measures appear to still be the most used features when dealing with myoelectric signal classification and sEMG-based hand gesture recognition [2,5,7,9]. However, due to the well recognized non-linearity of said kind of bio-signal [1,16], non-linear time-series analysis was also introduced for myoelectric signal processing and feature extraction [18,26]. In [18] the authors compared the performancesof Higuchi's fractal dimension and of the detrended fluctuation analysis with respect to a limited set of classical sEMG features, showing promising results if such kinds of non-linear measures are applied on weak sEMG signals acquired from the upper limbs. Later, in [17] an extended set of fifty features belonging to both time and frequency domains was examined, further adding a series of non-linear measures, including a box-counting dimension, critical exponent analysis, a Katz fractal dimension, approximate entropy and sample entropy. The latter appeared as the best single feature among the other ones and its use together with another four features showed the best performance as a multiple-feature set. In particular, fuzzy entropy (FEn) was developed by [PERSON] and colleagues [16] for sEMG signal characterization, showing better performances with respect to approximate and sample entropy in distinguishing among arm gestures [16]. However, its validity was investigated for high quality sEMG signals, acquired with a gold-standard system in a single-differential probe setup, while also considering a relatively limited set of hand gestures [16; 27], and its validity in the hand gesture characterization field has not been investigated in further works, except for a comparison with different kinds of complexity measures [27]. The FEn relies on a fuzzy function to assess similarity, while more classical entropy measures are based on binary logic decision rules, but they still each adopt a distance metric to compare subsequent samples [16].