## 4 Discussion
This appears to be confirmed also for the two entropy measures investigated in this study, considering that both FEn and PEn showed a relatively consistent behavior in assessing complexity of the same kind of signal with different numbers of data samples [32; 39]. However, the latter does not imply that the issues related to data length and sampling rate are completely negligible because it is obvious that too few data samples could not allow a proper exploitation of the underlying structural characteristics of the signal,leading to poor reliability of entropy measurements [49]. On the other hand, it is straightforward to assume that data acquired with quite different sampling rates, e.g., 1 Hz and 10 kHz, could lead to different entropy measurements as well, since with a limited recording frequency the inherent dynamics of the system under consideration would likely be captured with less accuracy and some structural characteristics in terms of patterns repeatability could be lost [49]. Albeit it is not possible to determine a priori a suitable number of samples, a recommended rule of thumb is to have at least 200 data points in order to ensure the validity of entropy computation [50; 51]. In this study, feature extraction was performed on sEMG bursts related to gesture execution, which lasted at least 3 s, thereby producing myoelectric time series of 600 samples, much higher than the minimum number of samples reported above. Further, downsampling raw data represents a common procedure when dealing with entropy-based analyses, aimed at reducing computational loads without degrading the reliability of the outcomes [51]. Therefore, for all these reasons, results of this study seem to not be affected by the relatively low sampling rate used for data acquisition and could likely maintain their validity if different recording devices were to be adopted. However, this aspect deserves to be investigated in further studies, focused on a direct comparison between different acquisition systems, considering, for instance, that high-density sEMG systems are also employed for hand gesture recognition purposes [4]. It is worth noticing that the armband channel selection was based on the lowest signal-to-noise ratio, thereby discarding the information provided by the remaining sensors. However, the integration of sEMG signals from all the Myo sensors could further improve the gesture characterization, providing global information on the whole forearm's muscular activity. Due to the above-mentioned characteristics, the influence of armband positioning and thus probe placement on the myoelectric signal recorded by the device represents an additional topic of interest which warrants careful investigation in dedicated studies. This appears to be of particular importance considering that the Myo armband could present poor placement repeatability between experimental sessions [15] and further sensor misplacements can involve not only shifts around the forearm but also proximal and distal probes misalignments, which likely would degrade the sEMG signals recorded by the Myo armband. Additionally, the use of different entropy measures should be evaluated in future studies, in order to assess whether the sEMG could be better characterized by complexity measured based on time-series dispersion or sample distribution [28; 29].