## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Set** & **Original** & **Original + FEn** & **Original + PEn** & **Original + FEn + PEn** \\ \hline \multicolumn{5}{c}{Set 1} \\ \hline H & \(55.4\pm 6.4\) & \(71.0\pm 9.4\) & \(66.8\pm 8.6\) & \(80.0\pm 8.5\) \\ D & \(62.0\pm 5.3\) & \(75.7\pm 7.5\) & \(70.9\pm 5.0\) & \(82.3\pm 7.3\) \\ HD & \(65.8\pm 5.1\) & \(78.3\pm 7.0\) & \(76.0\pm 6.3\) & \(85.6\pm 6.3\) \\ HDF & \(74.5\pm 9.5\) & \(83.3\pm 11.9\) & \(82.5\pm 8.8\) & \(88.2\pm 7.4\) \\ \hline \multicolumn{5}{c}{Set 2} \\ \hline H & \(64.8\pm 10.7\) & \(79.6\pm 14.0\) & \(77.3\pm 9.2\) & \(87.1\pm 9.2\) \\ D & \(68.4\pm 10.0\) & \(80.2\pm 13.1\) & \(80.2\pm 7.6\) & \(85.7\pm 8.8\) \\ HD & \(73.3\pm 10.7\) & \(85.6\pm 9.8\) & \(83.4\pm 6.4\) & \(90.0\pm 5.3\) \\ HDF & \(83.8\pm 8.3\) & \(90.2\pm 6.5\) & \(89.6\pm 5.4\) & \(92.5\pm 7.2\) \\ \hline \multicolumn{5}{c}{Set 3} \\ \hline H & \(64.4\pm 11.2\) & \(81.0\pm 14.1\) & \(78.2\pm 9.2\) & \(88.7\pm 8.3\) \\ D & \(67.4\pm 10.1\) & \(80.8\pm 13.7\) & \(81.0\pm 7.6\) & \(86.6\pm 8.7\) \\ HD & \(72.9\pm 11.3\) & \(87.3\pm 8.8\) & \(84.0\pm 6.4\) & \(91.0\pm 4.6\) \\ HDF & \(84.7\pm 8.2\) & \(91.9\pm 4.0\) & \(91.0\pm 3.3\) & \(94.7\pm 2.3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy (%) obtained for each set of gestures using four different feature sets and a LDA classifier. Original indicates the feature set without FEn and PEn. Values are reported as average \(\pm\) standard deviation. Regarding the other parameter for FEn computation, all the considered \(r\) values allowed us to distinguish the expected number of gestures (see Section 3) and the goodness of clustering appeared poorly affected by its numerical values: the average distance of each point from the respective cluster showed almost the same value, for each \(r\) and each gesture set (Figure 5). This aspect highlights the validity of using the FEn as a possible feature for EMG-based gesture recognition, and when dealing with poor quality signals such as those provided by the Myo armband. Further, it also suggests that the use of FEn can be suited for the description of hand gestures which present different functional characteristics with respect to [16], involving indeed pure finger movements (set 1) and complex gestures which mimic routine daily activities (set 3). The same holds also considering the distances of all points from every cluster (Figure 6), where in addition the clusters for the first set of gestures were more spaced with respect to the other sets (Figure 3). This indicates that by using the FEn together with an off-the-shelf device for forearm sEMG signal acquisition, it might be possible to distinguish subtle hand gestures involving single-finger movements in addition to the more common hand and wrist movements [14; 16], considered in set 2. The PEn was introduced [30] as a tool for describing time-series complexity, based on a mapping of neighboring values to ordinal patterns, and it appeared to be a promising measure for characterizing sEMG signals, being able to correctly distinguish hand gestures within all the considered sets (Figure 4). However, as for the FEn, the computational parameters play a significant role. An embedding dimension of seven did not allow us to recognize the expected number of clusters in any of the three gesture sets (see Section 3). This represents an easily expected result, considering that with a sampling rate of 200 Hz and an epoch of muscular activation equal to about 3 s, the length of the considered time-series was at most 600 samples, far smaller than the 5040 samples required to satisfy the constraint \(d!\ll N\) (see Section 2) for having a reliable PEn estimation [32]. For the remaining embedding dimensions, the clustering worked better when \(d=5\) (see Section 3), particularly for set 2.