# EMG-Driven Shared Control Architecture for Human-Robot Co-Manipulation Tasks +
## 3 Results
### Experimental Results  
To validate the overall architecture by triggering all the envisioned state transitions, the following representative sequence of operations characterized by several human-robot and human-environment interactions is requested to the user: (\(i\)) drive the end-effector closer to a surface using _no-contact_ mode; (\(ii\)) switch to _contact_ mode to enter in contact with the surface; (\(iii\)) follow a predefined circular path drawn on the surface; (\(iv\)) lift the end-effector to switch to _no-contact_ mode; (\(v\)) bring the end-effector in contact with the center of the circumference; (\(vi\)) change the robot operational mode to _hold_ and push the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline \hline \multicolumn{3}{c}{Control parameters} \\ \hline \(K_{\text{ik}}\) & \(\text{diag}\{20\ I_{3},15\ I_{3}\}\) & Inverse kinematics gain \\ \(K_{\text{jc}}\) & \(\text{diag}\{3\ I_{4},2\ I_{3}\}\) & Joints controller gain \\ \(k_{d,\text{low}}\) & 80 & Low-damping gain \\ \(k_{d,\text{high}}\) & 1000 & High-damping gain \\ \(k_{k}\) & 100 & Stiffness gain in _hold_ mode \\ \hline \multicolumn{3}{c}{Classifier parameters} \\ \hline \(C\) & 1 & Regularization parameter \\ \(\gamma\) & ‘_scale_’ 1 & Parameter of a Gaussian Kernel \\ _Kernel_ & ‘RBF’ & Kernel used in the SVM Classifier \\ \hline \hline \end{tabular}  
* ‘scale’ is a parameter that is automatically calculated as 1 divided by the product of the number of features and the variance of the feature vector.  
\end{table} 
Table 1: Experimental parameters used in the proposed architecture. end-effector to prove that it returns to the center; \((vii)\) lift the end-effector to return in _no-contact_ mode again. To test the generalization and the reproducibility of the approach, the designed experiment has been performed by a group of four participants (one female, aged 28, and three males, aged between 27 and 35), each of whom repeated it for 10 times. In accordance with the Declaration of Helsinki, all the participants provided signed informed consent, and the research protocol was approved by the Research Ethics Committee of the University of Cassino and Southern Lazio. The EMG sensors were placed on the same muscles of the dominant arm for all the subjects; however, considering the differences in the physical structure of the subjects, there were small displacements in the positioning of the sensors from one person to another to ensure that the sensors were placed as much as possible in the center of the considered muscles. Each of the four subjects performed the acquisition procedure described in Section 3.2 to obtain the training and test sets for classifier training. Subsequently, each subject obtained their trained SVM model, and the performance of the classifier was evaluated for each participant's model using a confusion matrix and the global indices computed with it. Table 1 shows the parameters for the controllers and the classifier used in the experiments, where all the admittance parameters were empirically determined in such a way that both the resonance phenomena can be dealt with and the co-manipulation can be made easier. A video showing some of the experimental runs of all participants is also provided at the following link ([[https://youtu.be/4](https://youtu.be/4) YZ64 SgFEro]([https://youtu.be/4](https://youtu.be/4) YZ64 SgFEro), accessed on 25 July 2025), and a selection of snapshots from the video of the experiment performed by Subject 1 is presented in Figure 4.  
In the following, the results of the classifier performances and experimental execution are discussed.