## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Body** & **Range of Motion** & **T0** & **T1** & _p_**-Value** & **Effect Size** \\
**Structure** & Ab-adduction & 57.9 \(\pm\) 16.1 & 64.9 \(\pm\) 24.1 & 0.008 & 0.432 \\ Shoulder & Flexo-extension & 61.0 \(\pm\) 12.6 & 62.5 \(\pm\) 20.6 & 0.038 & 0.344 \\  & Rotation & 66.4 \(\pm\) 14.9 & 70.7 \(\pm\) 23.2 & 0.006 & 0.447 \\ Elbow & Flexo-extension & 81.7 \(\pm\) 15.8 & 84.4 \(\pm\) 26.9 & 0.033 & 0.352 \\ Forearm & Prono-supination & 108.7 \(\pm\) 26.2 & 112.5 \(\pm\) 36.1 & 0.037 & 0.345 \\ Hand & Closure & 40.7 \(\pm\) 11 & 43.2 \(\pm\) 14.7 & 0.014 & 0.405 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean \(\pm\) standard deviation (SD) of ranges of motion (ROM) at T0 and T1, compared by Wilcoxon test (_p_-values are reported with effect size). working volume, but a not clear interpretability of its change, as well as for the weight support. On one hand, these results should be interpreted considering the limits having computed a total score of MAS for obtaining a general level of spasticity of the upper limb, and having not measured the speed of movements, a key factor of which could be spasticity, a velocity-dependent phenomenon [23]. On the other hand, a similar trend was observed for weight support also in relation with other clinical scores. In general, we choose generic clinical scales commonly used for describing the patients' health status before and after the therapy, but more specific clinical scores could be used to link specific arm functions to the robotic parameters. Another limit of our study was that the patients followed personalized, and hence different, therapies over the two-week period. Despite the above limits, the results showed significant relationships between robot parameters and clinical scores, but these were more complex than expected, probably because they depend on many factors that are not mutually exclusive. The patients probably improved their abilities from a clinical point of view, but also in terms of the specific ability to manage the upper limb movements during robotic training and assessment. Thus, as previously stated [19], there is a need to consider the therapist within the patient-robot loop, defining three elements in this complex relationship. To solve the effectiveness paradox, personalization of therapy should be based on a unique, clear neuroscientific framework. It could also define the best set-up in terms of kinematic and kinetic robotic parameters for each clinical condition, for example, by defining when it is better to train the patient on a large volume with a high support, and when it is better to have a small volume with a lower support. However, although the possibility to set up the working volume, the single joint ROMs and the weight support allows the therapist to tailor the robotic therapy, these parameters seem too few to highly personalize the therapy with respect to the heterogeneity of stroke. Nonetheless, some improvements were observed, and it could also depend on the possibility given to the therapist to select the exergame that the patient should perform during the robotic therapy. Further studies should investigate the cognitive and motor aspects involved in these exergames, that probably introduced many more variables to adapt than the biomechanical robotic parameters alone. Future robots should also provide kinematic and kinetic information extracted by arm and hand trajectories and torques, respectively. The technology for embedding sensors with this scope in the robots already exist, such as wearable devices for motion capture [24] and for interacting with virtual reality [25]. Another interesting aspect is that, despite the robot allowing tridimensional movements, the tasks proposed during robotic rehabilitation are displayed on a 2D monitor. This monitor may limit the movements along antero-posterior axis, as shown in the example of Figure 1, flatting them mainly on the frontal plane.