## 4 Experimental Evaluation and Results
### Results on MPII Dataset  
To evaluate the mean average precision of the approach, we use the MPII data set and achieved an average mAP of 0.950. The breakdown per joint is shown in Figure 13.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Exercise** & _S\&G Error_ & **Pearson’s Correlation** \\ \hline Leaning left & 0.019 & 0.982 \\ Leaning forward & 0.025 & 0.975 \\ Leaning backwards & 0.032 & 0.984 \\ Leaning sideways & 0.024 & 0.979 \\ Touching face & 0.029 & 0.971 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean Sprague and Geers’ error and Pearson’s correlation between recorded and IK-reconstructed joint positions for all subjects.  
Figure 13: Mean average precision (mAP) per joint.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Detection Confidence** & **PCK@0** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** \\ \hline Head & 0.96 & 0.98 & 0.99 \\ Shoulder & 0.90 & 0.93 & 0.94 \\ Hands & 0.82 & 0.87 & 0.92 \\ Knees & 0.68 & 0.75 & 0.82 \\ Legs & 0.55 & 0.66 & 0.74 \\ All keypoints & 0.88 & 0.92 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of PCK values when thresholding at different detection confidence rates.  
To further validate our results, we performed the analysis using different cross-validation schemes. As most machine-learning models improve their performance as the training set is expanded, the k-fold cross-validation may perform somewhat better than the cross-validation estimate suggests. As the difference in size between the training set used in each fold and the entire dataset is only a single pattern, leave-one-out cross-validation (LOOCV) is nearly unbiased. The results are presented in Figure 14. However, LOOCV has a high variance (so you would obtain very different estimates in repeated tests). As the estimator error is a combination of bias and variance, whether LOOCV is superior to 10-fold cross-validation depends on both quantities. This observation matches well with other similar studies [98].  
A Bland-Altman analysis of agreement [95] was performed between the spatio-temporal features. The results (see Figure 15 for the vertical line characterizing the posture, and Figure 16 for the horizontal line characterizing the posture) show good agreement between the spatiotemporal features of the gait for subjects calculated using RealSense and IK analysis. The measure of validity is how close the results are to the truth on average (i.e., the higher the percentage of error on average, the lower the validity). A 100% validity would result in a mean difference of 0, resulting in a relative systematic error of 0.  
Figure 14: The cross-validation graph of accuracy distribution: 10-fold cross-validation vs. LOOCV.  
Figure 15: Bland–Altman plot shows the agreement between spatiotemporal features (horizontal deviation in the position of the vertical neck line between Heads and SpineBase joints) for subjects calculated using RealSense and IK analysis. The reference line at the mean is represented by the red solid line, and the top and lower limits of agreement are represented by the two black dashed lines.  
The correlation between the changes in position between the video sequences of the frames is represented in Figure 17. the presented graph confirms the validity of position estimation using IK, while only for 7 frames out of 58, the correlation value was not significant (\(p>0.05\)).  
Figure 16: Bland–Altman plot shows the agreement between spatiotemporal features (vertical deviation in the position of the horizontal shoulder line between ShoulderLeft and ShoulderRight joints) for subjects calculated using RealSense and IK analysis. The reference line at the mean is represented by the solid line, and the top and lower limits of agreement are represented by the two black dashed lines.  
Figure 17: Correlation between position changes.