# EMG-Driven Shared Control Architecture for Human-Robot Co-Manipulation Tasks +
## 3 Results
### Experimental Results
In particular, the confusion matrices of the SVM classifier trained for each subject are shown in Figure 5, while the global classifier indexes calculated using the formulas presented in Equation (16) through Equation (19) are shown in Table 2.  
Accuracy scores range from 81.69% to 91.40% across the four subjects, indicating a strong overall classification performance, meaning that the majority of examples are classified correctly. Precision indexes show high values, from 82.16% to 95.18%, indicating few false positives, while the recall, which is generally good but lower than precision (from  
Figure 4: The series of images displayed above illustrates one of the experiments conducted by Subject 1. 76.61% to 89.28%), suggests that the classifier is too conservative in predicting positive labels. Finally, F1 scores range from 78.11% to 90.59%, confirming a solid precision/recall balance with values close to accuracy. The numerical results demonstrate a reliable and accurate classification capability, although further analysis would be useful to improve recall by reducing false negatives. As illustrated in Table 2, the performance indexes are, on average, higher for Subject 1 than for the other subjects, which can be attributed to the fact that she is more familiar with the procedure of acquiring datasets for training the classifier during interactions with the robot. This is due to the fact that she has carried out previous tests with the aforementioned setup, whereas the other subjects have merely followed the acquisition instructions without performing any preliminary tests. Despite this, the performance indexes are very high for all subjects and very close to the values obtained by Subject 1, who is to be considered the expert subject, and this attests that the adopted system is highly user-friendly, as also indicated in the NASA-TLX questionnaires ([[https://humansystems.arc.nasa.gov/groups/TLX/](https://humansystems.arc.nasa.gov/groups/TLX/)]([https://humansystems.arc.nasa.gov/groups/TLX/](https://humansystems.arc.nasa.gov/groups/TLX/)), accessed on 25 July 2025) completed by users. Indeed, the mean workload score across all dimensions for the task in question is approximately 34%, indicating a relatively low overall workload. This suggests that participants generally found the task to be manageable and not onerous. Class-wise analysis of the confusion matrices reveals that the _up_ class was more frequently misclassified across participants. This may be due to overlapping activation patterns between _up_ and _free_, as both involve wide arm movements. Enhancing feature extraction or adding IMU-based motion data may reduce this confusion. On the other hand, it is clear that the _contraction_ class presents the higher average accuracy among the subjects, indicating that the muscle activation patterns are different enough with respect to the other envisioned classes. Figure 6 shows the path followed by the end-effector during one of the experiments performed by Subject 1, where the start and end points of the path are reported. Three different colors are used to highlight the path followed by the manipulator guided by the human operator, depending on the corresponding active operational mode: the paths executed in _no-contact_ mode are highlighted in green; those corresponding to _contact_ mode are highlighted in blue; finally, the path followed by the end-effector in _hold_ mode \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subject** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline S1 & 91.40 & 95.18 & 87.59 & 90.59 \\ S2 & 81.69 & 82.16 & 78.97 & 78.11 \\ S3 & 90.84 & 89.22 & 89.28 & 88.81 \\ S4 & 84.13 & 88.41 & 76.61 & 80.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table containing performance evaluation metrics. Figure 5: The confusion matrices obtained following the training of the classifier for all the participants: (**a**) Subject 1; (**b**) Subject 2; (**c**) Subject 3; (**d**) Subject 4.  
is colored in red.