## 4 Results
### Statistical Analysis  
Here, we report the statistical analysis related to the \(n=12\) unimpaired subjects. We provide global graphs that also include the indices for the two SCI subjects; however, they were excluded from the statistical analysis due to their limited sample size and the differences in their injury levels, which prevent meaningful statistical comparisons. For clarity, throughout the analysis, their data are presented for visual inspection only: the tetraplegic subject is identified by the red dot, and the paraplegic subject by a green one. Figure 5(a)-c show the box-plots for the three metrics before and after training. Distance accuracy significantly increased (\(p=0.006\)), suggesting that participants became more precise in navigating the robot toward the targets. Similarly, touch accuracy showed a significant improvement (\(p=0.002\)), indicating enhanced ability to align the robot base or end-effector with the target centers after training. In general, an enhancement in the successful touches across subjects can be observed, highlighting a tendency to improve after the training session. However, the normalized time metric did not exhibit a statistically significant change (\(p=0.774\)), suggesting that, while participants became more accurate in reaching targets, the time efficiency did not notably improve, potentially due to a cautious approach in achieving higher precision. It is possible that users prioritized accuracy over speed, especially after becoming more familiar with the system. Figure 6: Box-plots of the three metrics, accuracy \(A\) (**a)**, time \(\bar{t}\) (**b**), and touch accuracy \(A_{t}\) (**c**) before and after training calculated based on data of the 12 unimpaired subjects. The red line indicates the median values of the metrics across unimpaired subjects, while the dots represent the two SCI subjects. For the third target, accuracy was analyzed using both the robot base and the end-effector minimum distances. Figure 6(a) represents the distribution of the accuracy between the base and the third target. Figure 6(b) shows the distributions of the accuracy in terms of end-effector distance from the third target. After training, one outlier is identified, which corresponds to subject S05. The outlier was identified as the data point falling outside the lower whisker of the box-plot, meaning it is more than 1.5 times the interquartile range below the lower quartile. In our test, this indicates that the minimum distance between the end-effector and the third target was much larger than that achieved by other subjects, suggesting poorer performance in controlling the end-effector. This results in a null success rate for this subject in reaching the third target, as further clarified in the next subsection. However, since the overall performance of this outlier still shows improvement before and after training--except for the end-effector accuracy metric (Figure 6(b))--we decided to include this subject in the subsequent analyses. The increased accuracy for the third target when using the robot base (\(p=0.006\)) and the end-effector (\(p=0.039\)) confirms that participants improved their control and spatial awareness, even in more complex tasks. For the two SCI participants (red dot for SCI 1 and green dot for SCI 2), we observe that SCI 2 performed worse than SCI 1. This can be attributed to the fact that the control system was primarily tailored to a tetraplegic injury level. Consequently, it was more customized for SCI 1, allowing for a better response to the smaller input signals typically associated with tetraplegic subjects. The results for SCI 1 are favorable, frequently showing performance above 50% and notable improvements between the pre-training and post-training phases. In contrast, SCI 2 often demonstrates metrics below 50%, which can be explained by the system's design being more suited to tetraplegic subjects.