# Effective Behavioural Dynamic Coupling through Echo State Networks
## 4 Results
### User Testing  
As a final step of the testing phase, the system was finally exposed to users. That is, eight people were asked to use the system and control the simulated wheeled robot without a specific task. Their only goal was to control the robot in the way they wanted. Results in this case are very similar to the ones observed with the _Dataset Testing_ condition. The ESN was able to find the right set of output weights for all sequences provided by all height users every time. Although the input sequences recorded by the users where completely arbitrary and very different in terms of overall length and gesture patterns, the proposed architecture was able to cope with the incoming signal and mapping it to the output. Notably, the overall training performance was significantly increased with respect to the previous testing, since the network had only to distinguish between four input patterns, i.e., the four gestures associated to the four pre-coded movement of the robot. Table 4 shows the lengths of the 4 recorded input patterns (gestures) from the participants. From the table we can observe the high variability of the gestures in term of length of the input and to also, therefore, highlight the capability of the setup to deal with different lengths. The input device was sampled at the maximum allowed frame rate (i.e., 100 fps) with the length of each sequence being the number of frames recorded from the user. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Description** & **Training** & **Testing** \\ \hline Push & 0.99 & 0.99 \\ Pull & 0.99 & 0.99 \\ Swipe right & 0.99 & 0.98 \\ Swipe left & 0.99 & 0.72 \\ Clockwise Circle & 0.99 & 0.89 \\ Anti-Clockwise Circle & 0.99 & 0.70 \\ Up-Down & 0.96 & 0.88 \\ \hline Mean & 0.98 & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training and testing accuracy scores for the 7 gestures. Score is measured as the mean of recognised time steps for each gesture. Furthermore, in order to provide a qualitative appreciation of the variability observed between user gestures, Figure 3 shows the training set (i.e., the recorded sequences made by the Leap Motion device) of 3 users depicted within the 3 respective graphs. Each figure shows 6 lines representing the 6 D.o.F of the input device during the recording of the user. Those recorded values are then fed to the ESN as input. In each figure, the separation of the four sequences, representing the four gestures made by the user, is indicated above the graphs with the label G1, G2, G3, and G4 respectively, referring to the forward, backward, clockwise, and anticlockwise movements of the robot. By visually comparing the patterns for the three users it is possible to appreciate their differences, both _within_ the same user and _between_ users. As already seen in Table 4, the differences in length of the sequences are noticeable. Moreover, it is also possible to appreciate the difference in which users have decided to associate their gestures to the four robot behaviours. Some users preferred periodic movements for all their input sequences (e.g., \(P6\)), while others chose more stable and non-periodic movements (e.g., \(P8\)). At the same time, as shown in \(P7\), the system was also able to handle cases were periodic and non-periodic input behaviours were mixed by the user. After the test, each user was asked to respond to a questionnaire, in order to investigate the quality of the interaction and the feasibility of the methodology proposed. It is to be noted, in fact, that given the characteristic of the task presented the subjects, it is not possible to disentangle the input sequences performed by the users at run-time with the corresponding output and isolate the single gestures recorded during the training phase by the users itself, in order to make a comparison. This makes impossible to assess the accuracy of the network in the same way as it was done for the _Dataset Testing_ condition.