# The Influence of Immersive Virtual Reality Systems on Online Social Application
## 5 Result
### Understanding of Body Language  
Table 3 records accuracy rate and mean time data of RVM-IVRSA body language expression recognition by users in our second part of the test. We first recorded recognition accuracy and average time spent (not counting the time used to identify failure) of the five actions provided by researchers (driving, reading, tennis, hugging, and clapping). There were a total of 45 kinds of actions that were independently selected by volunteers, and 75 tests were conducted. It was inconvenient to list too many kinds of test data. The test results of 45 actions in this part were not equal to preset actions in terms of test times and should not be used as comparison data. However, these actions had a strong randomness that was closer to real usage. Therefore, data of this part of the test were integrated and recorded in Table 3 with the label of \"Optional\". We regard it as reference data that represent real situation. By analyzing it alone, we could better understand the real usage level of body language in this social mode. Combining with preset actions data, we can see the impact of various movements on recognition rate. Figure 7 is drawn from the data in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RVM-IVRSA** & **WeChat** & **\(p\)-Value** \\  & **Mean/SD** & **Mean/SD** & **\(p\)-Value** \\ \hline
**Question 6** & 4.07/0.35 & 3.59/0.32 & 0.01 \\
**Question 7** & 4.00/0.36 & 3.55/0.26 & 0.04 \\
**Question 8** & 4.41/0.25 & 3.75/0.33 & 0.08 \\
**Question 9** & 4.17/0.36 & 3.58/0.32 & 0.02 \\
**Question 10** & 3.69/0.30 & 2.75/0.47 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Online social experience score.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Accuracy** & **Mean Time(s)** \\ \hline
**Drive** & 1.00 & 11.6 \\
**Read** & 0.53 & 18.2 \\
**Tennis** & 0.87 & 12.75 \\
**Hug** & 0.60 & 16.5 \\
**Handclap** & 1.00 & 7.2 \\
**Optional** & 0.55 & 20.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recognition accuracy and mean time of action.  
Table 3 and Figure 7 show that five pre-prepared actions had a higher recognition accuracy than \"Optional\" actions of volunteers. The accuracy of \"Drive\" and \"Handclap\" reached 100%, \"Tennis\" reached 87%, and the recognition time of these three movements was within 15 s. The recognition accuracy of \"Read\" action was only 53%, which we think was because this action not only needed a character model to imitate shape change, but also needed cooperation of other object models (such as books) to be reproduced accurately. When model shape was changed simply, pose estimation could easily be wrong. The \"Hug\" action was difficult to reproduce accurately using only three points of tracking with the head and hands, requiring a physical change in other parts of body, such as arms. From the data of \"Optional\", we can infer that in practice, the success rate of body language recognition was not ideal, and the usage time was also a lot. In feedback from volunteers after the experiment, we learned of several reasons behind this. In addition to the points described above, another important reason was that users were not familiar with this type of interaction and could not think of efficient body language movements quickly. Regarding this piece of feedback, we believe that RVM-IVRSA technology development is not mature, and that users need some experience to freely use all its functions; therefore, the use of wizards is very necessary for IVRSA based on motion capture.  
We obtained age, gender, and body language recognition rates for 30 users. We attempted to group the data according to age and gender to discuss the effect of users' age and gender on the experimental results in this experiment.