Ties
Summary and 1 Introduction
2 related work and 2.1 virtual avatars
Visualization of the cloud of 2.2 points
3 Test design and 3.1 Configuration
3.2 User representations
3.3 Methodology
3.4 Virtual environment and 3.5 tasks Description
3.6 questionnaires and 3.7 participants
4 results and discussions, and 4.1 User preferences
4.2 Task performance
4.3 Discussion
5 conclusions and references
4.2 Task performance
In this subsection, we present the analysis of the results collected from the users of the evaluation session. To assess the performance of user tasks between the different representations, we have collected data via newspapers. These data were: time, to assess the effectiveness of representation; Number of obstacles struck, for the evaluation of spatial consciousness. Figures 4 and 5 show times for each task in perspectives and representations. Given that the fourth sub-jam had a fixed execution time, we chose not to use time for this particular task. Instead, we used the number of bullets captured during the fourth sub-jacket (Table 3). The number of obstacles struck and the captured balls are found in Table 2. Due to the small quantity of obstacles in each task, we also used the percentage of collision avoidance as an additional metric in the comparison between representations.
In the following sub-sections, we present the results obtained for each of the measurements used (time, number of struck obstacles and captured bullets) for each of the subtaches.
4.2.1 task 1
Collision: Bringing together the results by perspective, we can say that users collided with more objects in the first person perspective with the point avatar against the abstract avatar (Z = -2.668, p = 0.008). In the perspective of the third person, we found a statistical meaning between the mesh and the punctual -cloud representation, the Avatar PointCloud having a higher number of obstacles avoided (Z = – 2.542, p = 0.011).
During the grouping of perspectives by representations, we can say that we have only found the statistical meaning in the punctual -cloud representation, the prospect of the third person with the higher number of obstacles avoided (Z = -3.490, p <0.005).
Time: During the execution of the first task, we found a statistically significant difference from the first -person perspective between the point cloud and the abstract avatar (p = 0.024), the summary having the best performance. It was also found between the abstract and meshing avatars (p = 0.015), the summary having the edge. By comparing the same representation for the two perspectives, a statistical meaning was found between all the avatars (summary: T (18) = – 6.312, p = 0; cloud: t (20) = – 3.254, p = 0.001; and network: T (16) = – 3.254, p = 0.005). For the three performances, the first -person perspective had the best performance.
4.2.2 Taste 2
Collision: In the test of representation in terms of representation, we found a statistical meaning, with the abstract avatar (Z = -2.714, p = 0.007) having the greatest number of obstacles avoided overall in 3 pp. In addition, between the avatars in a mesh and a point point (Z = -3.779, p <0.005) and between the abstract avatars and the mesh in the prospect of the third person. From the first person perspective, no significant statistical difference has been found. No service has been found between 1PP representations.
In addition, when you compare the different representations in both perspectives, we have found a statistical meaning for punctual avatars (Z = -2.941, p = 0.003) and to mesh (Z = -3.673, p <0.005), the first person having the advantage in both cases.
Time: No statistically significant difference was found between one of the combinations of people concerning the representation. By comparing the same representation and two different perspectives, it is only in the representation of the network, a statistically significant difference was found (T (18) = – 2.479, p = 0.023), the prospect of the first person having the advantage.
4.2.3 Task 3
Collision: In the task, no significant statistical difference was found when using the representation grouping factor. However, we found a statistical meaning in favor of the abstract avatar in 1PP against the same representation in 3pp (Z = -2.714, p = 0.007).
Time: On the third task, we found a statistically significant difference when comparing abstract representations (p = 0.05) and representations of points clouds (p = 0.045) at the mesh, but not between them. In both situations, the Mesh Avatar experienced a worst performance. Similarly to the first task, all representations experienced better performance in the perspective of the first person (summary: T (20) = – 6.76, p = <0.005; mesh t (18) = - 4.276; points cloud: T (19) = - 5.354, p <0.005)
4.2.4 Taste 4
For the task of capturing the ball, we found a statistical meaning in the first person between the occasional avatars and the mesh (Z = – 2.546, p = 0.011), with better results for punctual representation, and the abstract and mesh avatars (z = -2.401) Table 3, with the best for the avatar of Mesh
When you compare the representations between the perspectives, we have found statistically better results for the punctual cloud in the third person compared to the same representation in the first person (Z = -3.961, p <0.005).
