## 4 Discussion  
This study presents a comprehensive dynamic analysis, encompassing both kinematics and muscle activation, in healthy individuals and four SCI patients performing both real and virtual versions of the BBT. The goal is to deepen our understanding of how VR impacts motor control and muscle engagement. While numerous studies evaluate pre- and post-rehabilitation improvements with VR, there is a notable gap in research examining how VR affects mobility and muscular demands during use. Addressing this gap is crucial for designing VR-based rehabilitation games that replicate the physical demands of real-world tasks. Such alignment is essential for promoting occupational rehabilitation aimed at improving daily living activities, as emphasized by the World Health Organization [26]. Parameters related to hand trajectory during the BBT were analysed, building on previous studies that used these metrics obtained from kinematics recorded on a virtual platform where trajectories are performed prior to and after rehabilitation to detect motor control improvements achieved through motor-assisted rehabilitation in SCI patients [16]. Such rehabilitation has been shown to enhance smoothness by reducing velocity peaks (SM) and improve efficiency by shortening trajectory length (EM). This study reveals that, for both healthy participants and patients, virtual reality results in longer trajectories (EM) and higher velocity peaks (SM), likely due to challenges in controlling the virtual environment experienced by both groups. When comparing health conditions, patients exhibit worse outcomes--longer trajectories and higher peaks--during both the real and virtual BBT, reflecting the motor control difficulties faced by this population [8]. Effective \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{10}{c}{**Muscular Amplitude**} \\ \hline  & & \multicolumn{3}{c}{\(MA_{7}(t)\)} & \multicolumn{3}{c}{\(MA_{2}(t)\)} & \multicolumn{3}{c}{\(MA_{3}(t)\)} & \multicolumn{3}{c}{\(MA_{4}(t)\)} & \multicolumn{3}{c}{\(MA_{5}(t)\)} & \multicolumn{3}{c}{\(MA_{6}(t)\)} & \multicolumn{3}{c}{\(MA_{7}(t)\)} \\ \hline \multirow{3}{*}{Health} & \multicolumn{3}{c}{Factors} & Factors & Factors & Factors & Factors & Factors & Factors & Factors & Factors & Factors \\ \cline{2-13}  & \(t\) & TC & \(t\):TC & \(t\):TC & \(t\):TC & \(t\):TC & \(t\):TC & \(t\):TC & \(t\):TC & \(t\):TC & \(t\):TC & \(t\):TC & \(t\):TC \\ \hline Healthy & * & * & * & * & * & * & * & * & * & * & * & * & * & * & * & * & * \\ \hline \multirow{2}{*}{Patient} & \multicolumn{3}{c}{*} & \multicolumn{3}{c}{*} & \multicolumn{3}{c}{*} & \multicolumn{3}{c}{*} & \multicolumn{3}{c}{*} & \multicolumn{3}{c}{*} & \multicolumn{3}{c}{*} & \multicolumn{3}{c}{*} & \multicolumn{3}{c}{*} \\ \cline{2-13}  & \(t\) & HC & \(t\):HC & \(t\):HC & t & HC & \(t\):HC & t & HC & \(t\):HC & t & HC & \(t\):HC & \(t\):HC & \(t\):HC \\ \hline RBBT & & * & & * & & * & & * & * & * & & * & * & * & * \\ \hline VBBT & & & * & * & * & * & * & * & * & * & * & * & * & * \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Factors with significant differences (\(p<0.05\), marked with an asterisk) in the two LME analyses of muscular activation. The first LME analysis was conducted separately for each sensor and health condition, with time (\(l\)), test condition (\(TC\): RBBT and VBBT), and their interaction (\(t\):TC) as fixed factors. The second LME analysis was conducted for each sensor and test condition, with time (\(t\)) and health condition (\(HC\): healthy and patients) and their interaction (\(t\):HC) as fixed factors. Sensors: 1 WF&UD; 2 WF&RRD; 3 DF; 4 TM; 5 FE; 6 WE&UD; 7 WE&RD. virtual rehabilitation should aim to reduce these parameters (EM and SM) to levels more comparable to those of healthy participants. Additionally, efforts should be made to bring the virtual environment closer to real-world conditions.