# Upper Body Pose Estimation Using Deep Learning for a Virtual Reality Avatar
## 6 VR User Experiments' Results  
### Single-User Archery Game Evaluation  
We summarize the archery game's questionnaire results using our proposed method and an IK method in Figure 11. On a scale from one (very poor) to five (excellent), participants rate their experiences in the single-user VR game. We review the means of questionnaire results for each question. Figure 11 illustrates that the majority of the participants voted for our suggested method in Q1 (mean = 4.80), Q2 (mean = 4.86), Q3 (mean = 4.53), Q4 (mean = 4.73), Q5 (mean = 4.93), Q6 (mean = 4.80), and Q7 (mean = 4.93). Participants favored our method in all the questionnaires.  
We compare the post-questionnaire results using a paired \(t\)-test with significant importance of 0.05 for either accepting or rejecting the null hypothesis. The paired \(t\)-test results with \(p\) < 0.05 indicate that our method significantly differs from an IK method. Table 9 is the statistical analytics of the archery game questionnaire under our method and the IK method. Table 9 illustrates that the difference between our method and IK in Q1 (t = 4.582, \(p\) = 0.003), Q2 (t = 3.567, \(p\) = 0.003), Q4 (t = 2.167, \(p\) = 0.047), Q5 (t = 2.449, \(p\) = 0.028), Q6 (t = 2.167, \(p\) = 0.047), and Q7 (t = 3.674, \(p\) = 0.002) are substantial. The paired \(t\)-test results show that the \(p\)-value of Q3 is high, so the difference between our method and IK for a sense of moving is insignificant.  
The paired \(t\)-test reveals significant differences in the presence (Q1) and embodiment (Q2). This validates hypothesis H2 that our method of displaying the upper body in a single-user VR environment increases the feeling of presence more than the IK method. The \(t\)-test result of accuracy (Q4) and naturalness (Q6) tells that our method is significantly favorable with an IK method with \(p\) < 0.05 and confirms hypothesis H1 that our method generates natural-looking avatars in VR. The \(t\)-test result of the Q5 shows that our method has less delay with a significant importance of 0.028, and therefore confirms hypothesis H3 that our lower latency method enhances user experiences and presence. The \(t\)-test result of Q3 shows that our and IK's methods are similar. This result makes sense, considering that our experiments include the minimal moving of participants. Therefore, participants feel less sense of moving around in a virtual environment no matter what.  
Furthermore, we compare the points participants earned after playing the archery game under two modes. The bar chart in Figure 12 illustrates all participants' average points for two modes. With our pose regression solution, participants obtained average points of 42.66 (85.32% of the highest possible points) and 35.73 (71.46% of the highest  
Figure 11: Questionnaire results in the archery game. Our proposed method and IK.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Question**} & \multicolumn{2}{c}{**Group (Mean)**} & \multicolumn{2}{c}{**Ours-IK**} \\ \cline{2-5}  & **Ours** & **IK** & **t** & \(p\) \\ \hline Q1 & 4.80 & 4.20 & 4.582 & 0.003 \\ Q2 & 4.86 & 4.20 & 3.567 & 0.003 \\ Q3 & 4.53 & 4.20 & 1.434 & 0.173 * \\ Q4 & 4.73 & 4.26 & 2.167 & 0.047 \\ Q5 & 1.06 & 1.46 & 2.449 & 0.028 \\ Q6 & 4.80 & 4.38 & 2.167 & 0.047 \\ Q7 & 4.93 & 4.33 & 3.674 & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Statistical analysis of questionnaire of the archery game under our method and the IK method. * marks the questions with insignificant differences.  
possible points) using an IK solution. Participants obtained higher average points with our proposed pose regression method.