## 4 Experimental Results
### The Performance of 2D Skeleton Estimation  
Our proposed technique relies on the success of 2D skeleton extraction and alignment of corresponding joint depths to form 3D skeletons for classification. Though the OpenPose tool is featured for its capability in incomplete skeleton extraction in the cases where some joints are occluded by blankets, some failures are still possible and need to be excluded, for example: (c1) fewer than four specified joints are detected; and (c2) the detected four specified joints do not conform to reasonable bone-length relationships.  
For the volunteers, in total 3154 out of 3280 individually captured frames (96.16%) were successful in 3D skeleton extraction, since they follow the guidance of the professional caregivers to perform the demanded sleep postures. By contrast, lying postures of the clinic elderly patients were captured continuously at a 3-6 Hz rate. Table 10 summarizes the collection of the clinic samples in several visits, where only 3401 out of 7548 samples (45.06%) were successfully processed due to long burst failures.  
Figure 5 shows some typical failure examples in skeleton generation due to: (a) and (b), the blanket fully covers the shoulders; (c), the scarf has a similar color to that of the pillow; (d) the patient was woken and scratched her face for a while. All of the above situations result in a long burst failure as mentioned earlier. On the contrary, Figure 6 shows examples where 3D skeletons are extracted successfully.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Female patient—nose-centered joints**} & \multicolumn{2}{c}{**Female patient—nose-centered joints**} \\  & \multicolumn{2}{c}{**(2D skeleton plus depth)**} & \multicolumn{2}{c}{**(3D skeleton)**} \\ \hline  & \multicolumn{2}{c}{Pred.} & \multicolumn{2}{c}{Pred.} & \multicolumn{2}{c}{Pred.} \\ \cline{2-7} GT & left & supine & right & GT & left & supine & right \\ left & 99.06 & 0.94 & 0 & left & 95.91 & 2.83 & 1.26 \\ right & 0.82 & 1.64 & 97.54 & right & 0.82 & 1.91 & 97.27 \\ Accuracy: 97.34 & & & & & Accuracy: 96.55 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Male Patient—nose-Centered Joints**} & \multicolumn{2}{c}{**Male Patient—nose-Centered Joints**} \\  & \multicolumn{2}{c}{**(2D Skeleton Plus Depth)**} & \multicolumn{2}{c}{**(3D Skeleton)**} \\ \hline  & \multicolumn{2}{c}{Pred.} & \multicolumn{2}{c}{Pred.} & \multicolumn{2}{c}{Pred.} \\ \cline{2-7} GT & left & supine & right & GT & left & supine & right \\ left & 95.10 & 4.90 & 0 & left & 95.51 & 4.08 & 0.41 \\ supine & 0.62 & 98.76 & 0.62 & supine & 2.5 & 97.5 & 0 \\ right & 1.86 & 0 & 98.14 & right & 2.39 & 1.33 & 96.28 \\ Accuracy: 97.31 & & & & & Accuracy: 96.29 & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{Pred.} & \multicolumn{2}{c}{Pred.} \\ \cline{2-7} GT & left & supine & right & GT & left & supine & right \\ left & 100 & 0 & 0 & left & 99.37 & 0.63 & 0 \\ right & 0.27 & 0.27 & 99.46 & right & 0 & 0 & 100 \\ Accuracy: 99.71 & & & & & Accuracy: 99.71 & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Accuracy and confusion matrix (all numbers in terms of %) for Setting 5.