# Real-Time Motion Adaptation with Spatial Perception for an Augmented Reality Character
## 5 Experiments and Results
### Experiment 2: Qualitative Comparison with Co-Presence  
One essential way to evaluate AR environments is to measure the feeling of being in the space with the AR character [38]. For participant co-presence, we investigated how the character responded to two different conditions (adapted motion on or off) in two different environments (puddle or muddy conditions in a real environment). In the experiment, we presented the participants with given scenarios on a portable AR device, similar to those used in previous interaction experiments with virtual characters [10]. Most participants were university students; twenty people (15 men and 5 women) with an average age of 26.5 years were paid and participated, and they conducted the within-subject experiment, which was conducted in a one-time session. Also, for consistency, it was performed at the same time on several days under the same weather conditions (i.e., sunny days). Here, all participants reported that they had prior experience with immersive virtual reality headsets. In the experiment, the AR participant used the Galaxy Tab S8 Ultra 5G computing device, which can be easily used in the outdoor environment as previously mentioned. To measure the feeling of being in the same space with the AR character, which is called co-presence, the AR character was naturally operated via a context-aware control method to adapt its motion to the real environment. After the participants had completed all tasks, their perceptions were measured using a qualitative questionnaire to allow them to express how much they agreed or disagreed using a seven-point Likert scale [39]. Figure 8 shows participants' co-presence results generated for the different conditions (i.e., adapted or nominal/original motion) and scenarios (i.e., puddle or muddy situations) with a pairwise comparison using two-sample t-tests. In the puddle situation, the two-sample t-tests revealed main effects at \(p<0.05\) in the level of co-presence (see Figure 8 left). However, we found no statistically significant main effect in the muddy situation (see Figure 8 right). In certain cases of our experimental results, participants showed a greater co-presence when adapted motion was used. When asked about the reason to evaluate the resulting score; 12 participants (60% people in participants) answered that they evaluated it based on similarity to the actual situation. For example, some subjects answered that the character's motion should be adapted to certain situations similar to everyday life. In our results in the puddle examples, the participants thought that the AR character would walk carefully to avoid splashing. On the other hand, in the muddy case, they answered that the character should not walk in the muddy space but has to take a different path. Accordingly, we found that additional research work on interaction responses of a virtual object such as a 3D character depending on the situations in daily life will be needed. Thus, in the future, we will need to analyze situations where higher co-presence can be achieved. For example, we will try to find out where automatic motion adaptation can be helpful in real-world scenarios.  
Figure 8: Survey results regarding participants’ experience of co-presence: adapted motion versus motion without adaptation (or nominal/original motion). Here, scenario #1 involved a puddle, and scenario #2 was performed in a muddy situation.  
Also, through open follow-up questions of user feedback, we also learned that participants' experiences can significantly influence co-presence in AR environments. For example, participants who had developed mobile AR thought that the AR character should naturally have adapted motion to real-world situations; otherwise, they said co-presence with the AR character was broken. Therefore, future research should be conducted to analyze the impact of the accuracy level of the character motion according to user experience, and research on motion editing for natural movements will also be needed.