## 4 Experimental Evaluation and Results
### Robustness Evaluation and Crossvalidation on KIMORE Dataset  
The robustness of our system was evaluated using the kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation (KIMORE) dataset [99], which includes RGB depth videos, as well as skeleton joint position and orientations, including specified characteristics, as well as physician assessment or score. We used only RGB materials from the dataset to evaluate performance. No material from this dataset was used to train our model. The KIMORE dataset includes a large diverse population of 78 participants separated into two groups. Group I (n = 34) participants with motor dysfunctions (back pain, after stroke, and Parkinson's disease) and group II (n = 44) healthy participants. Each participant completes five distinct workouts. We did not classify per these three classes as the dataset was imbalanced in this regard, the main focus was to check the accuracy of 3D recreation of each movements performed. The movements are as follows:  
1. Arms being lifted;
2. A lateral tilt of the trunk with arms extended;
3. Rotation of trunks;
4. Transverse plane pelvic rotations;
5. Kneeling. The results are displayed in Table 5 and the plot of correctly and incorrectly performed exercizes is displayed in Figure 18.  
We tested our final model with fixed-length input after training it with variable-length data. We generate four fixed-length versions of identical test data (100, 200, 300, and 400 frames) as suggested by [100]. These fixed-length versions depict rehabilitative exercises done at various (slow/fast) speeds and repetition counts. As input, we extract the latent feature representation for variable-length and fixed-length test data. Figure 18: Plot of correctly and incorrectly performed exercises. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Exercise** & **Correlation** & **CCC (95\% CI)** & **ICC (95\% CI)** & **Bias (95\% LoA)** & **PE (\%)** & **Repeatability (\%)** \\ \hline Arms being lifted & 0.81 & 0.77 (0.56–0.89) & 0.87 (0.72–0.94) & \(-\)2.81 (\(-\)17.15–11.54) & 23.98 & 13.08 \\ Lateral tilt & 0.94 & 0.88 (0.77–0.94) & 0.93 (0.83–0.96) & \(-\)0.91 (\(-\)3.79–1.97) & 13.05 & 15.14 \\ Rotation of trunks & 0.85 & 0.80 (0.62–0.90) & 0.89 (0.76–0.95) & \(-\)2.24 (\(-\)13.16–8.68) & 20.71 & 13.69 \\ Pelvic rotations & 0.84 & 0.80 (0.62–0.90) & 0.89 (0.75–0.94) & \(-\)2.32 (\(-\)13.69–9.06) & 21.15 & 13.61 \\ Kneeling & 0.79 & 0.76 (0.54–0.88) & 0.86 (0.71–0.94) & \(-\)3.02 (\(-\)18.66–12.62) & 25.22 & 12.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The Pearson’s correlation, CCC and ICC (with respective confidence intervals), Bland-Altman bias and LoA’s, percentage error (PE), and repeatability coefficients. To visualize multidimensional feature data, we use t-SNE [101]. t-SNE is a nonlinear dimension reduction method that allows high-dimensional data to be visualized by assigning each data point a location on a lower-dimensional projection. Each multidimensional sample is modeled by the t-SNE so that similar samples are mapped to nearby points, and dissimilar samples are mapped to distant points. The algorithm can capture the local structure of the dataset while revealing its global structure, such as any clusters. The 2D t-SNE plot of numerous workouts from the KIMORE dataset is shown in Figure 19. Our model gives equivalent feature representations for varying input durations, as can be shown. Based on the results of our pilot study, which compared the movements of disabled and healthy participants, it is clear that our model can successfully assess physical rehabilitation exercises/movements regardless of participant anthropometric parameters, the presence of imbalances or incorrect posture, compensatory mechanisms or slow motion. The model accurately assesses deviations from the norm or accuracy of the movement regardless of how many repetitions or how slowly the subjects performed the motion sequence.