## 4 Discussion
The results of this study showed that with a proper parameter settings, both FEn and PEn are able to provide a clear separation in all the three considered sets of gestures, which likely would improve the performances of a classification architecture fed by this kind of feature. Thus, the entropy-based feature investigated in the present study could be used in conjunction with more classical and well-acknowledged features [3; 9], giving a more consistent description of the sEMG patterns characterizing specific motor tasks. The latter aspect seems to be supported by the comparison of FEn and PEn with a set of 18 classical time and frequency features, in terms of predictive importance score (Figure 7). The analysis performed through the mRMR algorithm showed that for two out of three sets of gestures (set 1 and set 3) the proposed complexity measures were those with the highest scores (Figure 7a,c); there was a non-negligible drop in score for the third one in both cases. Thus, both the proposed complexity measures appeared to fall within the subset of features able to better characterize subtle movements involving finger numbering and relatively complex grasping. Considering set 2, FEn and PEn were within the five most predictive features (Figure 7b) and limited differences in their scores with respect to the first one were recognizable (0.016 and 0.013 versus 0.022). This could indicate that the FEn and PEn share with classical time domain features a significant and almost comparable predictive importance, which can enhance the characterization also of gestures related to forearm and wrist movements. In addition, it deserves to be underlined that irrespective of the gesture set, the SEn was one of the five most important features (Figure 7), confirming its validity as an sEMG feature for upper limb movement recognition [17] and at the same time supporting the use of complexity measures such as those considered in this study in the field of hand gesture characterization. The results of the analysis based on the predictive importance scores seem to be confirmed by the investigation of FEn and PEn in terms of classification accuracy (Table 2). The addition of the FEn alone and the PEn alone to each of the four considered feature sets led in any case to a substantial increase in the classification accuracy. It is worth underlining that also the accuracy of the HDF set, which comprises both time and frequency domain features, was noticeably enhanced by the addition of even a single entropy measure, reaching values beyond 80% for the first gesture set and beyond 90% for the other two sets (Table 2). The classification accuracy allowed us also to perform a comparison, even if indirect, between FEn and PEn, in order to gain information about whether one of the two complexity measures could be considered as the most appropriate to be included in a feature set for gesture recognition. For all the feature sets, the inclusion of the FEn appeared to provide a limited increase in the classification accuracy with respect to the PEn, with a difference in any case not greater than 5% (Table 2). However, it should be noted that when FEn and PEn were added at the same time within a feature set, the accuracy showed a dramatic improvement, as it did for the H feature set, which included the lowest number of features. In the latter case the accuracy raised up to 80% for set 1 and reached almost 90% for sets 2 and 3 (Table 2); for the HDF set, which encompassed the both time and frequency features, the accuracy increased beyond 90% for sets 2 and 3 and up to 88% for set 1. Further, the best accuracy was attained when both entropy measures were employed as myoelectric features, since in the latter case the accuracy was noticeably greater with respect to when FEn and PEn were considered individually.