## 4 Experimental Results
### Results
#### 4.3.2 Domain Adaptation  
As mentioned earlier, we collected data from 20 volunteers and two clinic patients. Although the volunteers imitated the postures of the bedridden patients, there were some inevitable dissimilarities. In this situation, volunteers and real patients belong to two different domains and the domain adaptation performance will be evaluated here. In Setting 2, the training set contains only the volunteers, while the testing set contains all the clinical data. Table 5 shows the results for individual male and female patients, when nose \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Skeleton-Based Posture Classification**} \\ \hline \multicolumn{3}{c}{**Training Data**} & \multicolumn{2}{c}{**Testing Data**} \\ \hline \multirow{3}{*}{Setting 5} & Partial clinical data (1116 samples): & \multirow{3}{*}{
\begin{tabular}{c} The remaining clinical data (1465 samples): \\ male (left, supine, right) = (372, 372, 372) \\ female (left, supine, right) = (186, 372, 186) \\ female (left, right) = (186, 186) \\ \end{tabular} } \\ \hline \multicolumn{3}{c}{**RGB image-based posture classification**} \\ \hline \multicolumn{3}{c}{**Training set**} & \multicolumn{2}{c}{**Testing set**} \\ \hline Setting 6 & 19 volunteers (without blanket) & One volunteer (without blanket) \\ \hline Setting 7 & 19 volunteers (with blanket) & One volunteer (with blanket) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._centered coordinates of joints are adopted. It reveals that the training set involving only the volunteer samples is not capable of transfer learning when the testing set contains the clinic samples. This poor performance is obvious for the male patient, due to the caregiver's improper arrangement or small depth difference between left and right shoulders (thus incurring a reverse ordinal relationship between them). Table 6 shows the average depth differences between the left and the right shoulder joints (\(\mathit{diff}=\text{depth\_R--depth\_L}\)) of three postures for the volunteer, the male patient, and the female patient. For left-sided, the pillow is put under the right side of the back and the right shoulder is closer to the camera, and, hence, the right shoulder depth is smaller than that of the left shoulder. Therefore, the \(\mathit{diff}\) value is negative for left-sided and positive for right-sided. Table 6 shows that \(\mathit{diff}\) for the volunteer are \(-131\) and \(118\) mm for left-sided and right-sided, respectively. However, the \(\mathit{diff}\)s for the male patient are \(-16\) and \(29\) mm for left-sided and right-sided, respectively. The \(\mathit{diff}\)s for the male patient are much smaller than those of the volunteers. For the female patient, \(\mathit{diff}\)s are \(-86\) and \(115\) mm for left-sided and right-sided, respectively. The \(\mathit{diff}\)s for the female patient are closer to those of the volunteers. This explains why the recognition accuracy of the female patient is better than that of the male patient. To increase the robustness and the adaptation of the proposed technique, in Setting 3, not only the volunteer samples but also partial clinic samples were used during the training stage. The accuracy and the associated confusion matrix are summarized in Table 7. Compared to Table 5, it indicates that the performance is improved by adding partial clinical samples for training. For the male patient and the female patient, the accuracy is \(66.71\%\) and \(86.02\%\), respectively, for \"2D skeleton plus depth\". In Setting 4, the training set includes more clinical data, and the performance is listed in Table 8. The accuracy is improved, as expected, since more characteristics of the clinical data are learned during training. It means that the impact due to the gap between the clinical data and the volunteers will be eliminated once the system is trained with more and more clinical data. Specifically, the accuracy for the male patient was boosted from \(66.71\%\) to \(94.52\%\) for \"2D skeleton plus depth\".