## 4 Discussion
These differences may reflect the increased motor control challenges faced by patients in adapting to the virtual environment, where the absence of tactile feedback and reliance on visual and proprioceptive inputs could lead to altered and less efficient muscle-activation strategies. This highlights how the test condition significantly influences motor performance in patients compared to healthy individuals. Consistent with the results of sEMG waveform-based parameters, the analysis of muscle activity revealed significant differences for all parameters in both health conditions, depending on the test condition. Values in healthy participants were consistently lower during the VBBT, which was expected given the absence of a real grasping action. Conversely, most values were higher in patients during the VBBT. This discrepancy may stem again from the greater motor control challenges patients face in the virtual environment, potentially leading to increased muscle activation to compensate for reduced tactile feedback and coordination difficulties. Although no general differences were observed when analysing each test condition group comparing health condition, patients showed lower median activation in some movements during RBBT, reflecting reduced motor efficiency and higher activation in digit flexion during VBBT, likely due to compensatory effort from the lack of tactile feedback in the virtual environment. These observations highlight the importance of tailoring virtual rehabilitation strategies to address these differential motor demands. This study extends the analysis of temporal patterns to muscle activation, complementing insights from kinematic synergies. Healthy participants exhibit consistent patterns of muscle activation across sensors (Figures 8 and 9), except for thumb movement (sensor 4), reflecting their adaptability to motor demands. Global differences due to test conditions (e.g., _MA1(t)_, _MA2(t)_, _MA4(t)_, _MA7(t)_) are consistently observed across frames. Despite no patterns being found for patients, the temporal evolutions of the patients differ from those of the healthy individuals, limiting their ability to maintain smooth activation. Variability in VBBT aligns with kinematic findings of increased peaks and less smooth trajectories, reflecting compensatory movements due to impaired motor control and the absence of tactile feedback. Healthy participants exhibit higher activations in RBBT. These results emphasize the importance of rehabilitation approaches to enhance motor control and feedback integration, aiming to align patient performance with healthy patterns. Drawing upon previous studies where kinematic data from the LMC has been effectively employed to classify daily activities through machine learning during grasping tasks [30], it could be plausible that hand tracking with appropriate machine-learning techniques could be used to detect kinematic patterns. Future research could leverage hand kinematics recorded directly from the LMC during rehabilitation exercises to analyse and classify levels of improvement, enabling adaptive difficulty adjustment. This approach would eliminate the need for external devices, such as the CyberGlove used in this study, to identify kinematic differences. However, while LMC-based kinematics provide valuable insights, electromyographic signals also offer critical information that requires external sensors to capture, emphasizing their complementary role in rehabilitation assessment. One limitation of this study is the fixed order in which test conditions were administered, which could potentially influence the results, particularly in the sEMG measurements. Future studies should consider randomizing the order of RBBT and VBBT to minimize potential order effects and reduce the influence of learning or fatigue bias. Another limitation is the inclusion of only right-handed participants, which may limit the generalizability of the findings to left-handed individuals, who may exhibit different motor control patterns.