# Upper Body Pose Estimation Using Deep Learning for a Virtual Reality Avatar
## 5 VR User Experiments
### Questionnaires  
We ask participants to answer seven questions for the archery game and five questions for the ball-grabbing game. Each question has to be answered on a five-point Likert scale from one (very poor) to five (excellent). For both tasks, most questions are about presence, embodiment, sense of moving, accuracy, and being natural. Tables 7 and 8 indicate our questions after the archery and ball-grabbing tasks. Figure 10: Ball-grabbing game in two modes. Camera view of three users (**bottom**) playing the ball-grabbing game using our method (**top**) and the IK method (**middle**). \begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **Question** \\ \hline Q1 & How strongly did you feel a sense of presence while using this method? \\ Q2 & How strongly did you feel a sense of embodiment while using this method? \\ Q3 & How compelling was your sense of moving around inside the virtual environment? \\ Q4 & How accurate was the environment for actions you are in? \\ Q5 & To what extent did you observe a delay between actions and the expected outcomes? \\ Q6 & How natural did your interactions with the environment seem? \\ Q7 & Do you prefer this method overall (for a single-user virtual game)? \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Questionnaire for the archery game. The participants' first task was to play a single-user game, the archery game. The single-user experiments were conducted in two sets, beginning with a hands-only mode, followed by an IK mode and our method mode in random order. Participants were not informed of the modes they were playing in advance. The hands-only mode was a trial, so a participant could get used to the game's rules. IK and our methods are the modes we evaluate and analyze. This task was tested on a PC with one HTC VIVE, two controllers, and two base sensor stations. The participants initially stood in an arrow zone of 110 cm by 100 cm in the set-up room and were guided to stay inside the arrow zone during the game. They then wore the HMD and stated any discomfort to ascertain the best experience. They posed similar to an archer with their hands and got ready to shoot toward the target. They moved one hand to shoot the arrow and the other to adjust the bow. The game was over when all the arrows were used, or when the time limit was exceeded. After finishing the first set of two modes, participants took a rest for the 60 s. Participants answered seven questions (Table 7) in seven minutes. This procedure was repeated for the other set. Then, groups of participants gathered to play a multi-user game, a ball-grabbing game. In the ball-grabbing game, three participants in a group throw and grab a ball at each other in a virtual shared space while they are physically located in different places. One room, where two participants played, was equipped with two PCs, two HMD devices, four controllers, and two base sensor stations, and the other room, where one participant played, was equipped with one PC, one HMD, two controllers, and two base sensor stations. Our experiment uses a client-server network. In other words, PCs with the HMDs are clients and communicate through a local server that a game engine provides over the game engine's network. So, network communication among VR participants begins with one HMD, goes through the connected PC, network router, and the VR virtual server of our game engine, and comes back to the network router and PCs connected to the other HMDs. Five teams of three participants played the ball-grabbing game. Each team played the ball-grabbing game in a hands-only mode to get used to the game. After 60 s of rest, participants were randomly assigned to play one of the two modes without knowing what it was and played the game in the assigned mode for 90 s. Participants rested for 60 s and filled out the questionnaire (Table 8) in seven minutes. Then, participants played the ball-grabbing game in the other mode for 90 s, rested, and filled out the questionnaire.