## 4 Discussion
Therefore, outcomes of this study seem to suggest the opportunity to consider both complexity measures within the same feature set, since each of them appeared to provide a different kind of information regarding the sEMG signal complexity, thereby avoiding feature redundancy. Incidentally, the latter aspect could be likely due to the different core logic behind the computation of FEn and PEn, the former being based on a distance metric to compare subsequent samples while the latter relies on the relative occurrence of an ordinal mapping of neighboring values (see Section 2.1). Few words deserve to be spent on the choice of analyzing data recorded from the Myo armband. Although this kind of device presents some technical limitations, e.g., a limited sampling rate which does not allow one to exploit the full sEMG signal bandwidth, it has been used as myoelectric signal recording system in a number of different studies focused on the topic of hand gesture recognition [2; 5; 11; 19]. The above-mentioned works comprise also the investigation of cutting edge algorithms in the artificial intelligence field, such as deep learning algorithms and recurrent neural networks [10; 13; 14]. Further, the Myo armband was also used for collecting databases of upper limb gestures [20] which became in turn popular benchmarks for testing gesture recognition architectures [13]. Hence, the use of the Myo armband as a recording device appears to be well assessed in the context of gesture recognition driven by sEMG signals [7]. On the other hand, the wide use of the Myo armband in the field of upper limb motion recognition, joined with the above-mentioned limitations, led to examining the device characteristics in terms of recorded signal and its processing. For instance, [PERSON] et al. [15] tested the long-period reliability of the Myo armband, while in [47] different filtering techniques were compared through the processing of Myo armband sEMG signals. It is noteworthy that despite the limited bandwidth, features in the frequency domain are commonly computed from myoelectric signals recorded by the Myo armband. Beyond the more classical MNF and MDNF [2; 5], measures related to the power spectrum were considered too [5], and in some cases the entire signal spectrogram was taken into account [48] along with features in the time-frequency domain, e.g., wavelet related measures [14]. The low sampling rate seems to not affect the validity of exploiting the information related to the frequency characteristics of the sEMG signal recorded by the Myo armband, and thus for gesture recognition purposes the use of devices with limited recording rates appears not completely prevented. Hence, all these aspects support the chance to assess the reliability of signal processing and computational techniques directly on data recorded from low-cost devices such as the Myo armband, since its technical limitations seem to not significantly affect its validity in the hand gesture recognition field. As reported above, the sampling rate of the Myo armband restricts the available bandwidth of the acquired signal, and thus the validity of the results presented in this study when using a proper sampling rate for myoelectric recording (1 kHz or above) requires brief discussion. Both FEn and PEn measure the regularity of a finite-length time series, in terms of pattern repeatability, and thus they do not belong to time or frequency domains [49; 50; 51]. Broadly speaking, entropy measures assess the existence of complex patterns in a time series, which describes the temporal development of a physical phenomenon, assigning a degree of repeatability lying in between two opposite extrema: a total randomness (e.g., white noise) and a total predictability (e.g., sinusoidal waves). Therefore, entropy measures deal with the existence of inner structures belonging to data distribution, mirroring in turn inner characteristics of a physical phenomenon which are not directly linked with the number of available data samples, which depend on each particular experimental setup [49].