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3.4. Results
can thus determine where the user is looking and more specifically, if he/she is looking at
a character or avoiding it. We then used Python scripts in order to, amongst other things,
assign animations to the characters on screen. Algorithm 3.2 summarizes the different steps
of the animation loop. We use two animation pools: one containing attentive behaviors and
one containing distracted ones. Each pool contains 10 different animations. Then, if the
eye-tracked coordinates are within the character bounds, an animation is randomly chosen
from the attentive pool and applied to the character. On the other hand, if the eye-tracked
coordinates are outside the character bounds, an animation is randomly chosen from the distracted
pool. A random time lapse is defined between the assignment of each animation. The
character thus does not become attentive or distracted immediately. Whenever an animation
is to be assigned to a character, we verify whether it is being looked at or not. Depending on
this, the chosen animation will be of type attentive or distracted.
Algorithm 3.2: Character behavior selection
Data: input point of regard on screen (POR)
Result: picked character
1 begin
2 Wait between 1 and 2 seconds
3 i = Randomly select an agent
4 j = Last selected agent
5 while i=j
6 do
7 i = Randomly select an agent
8
9
10
11
12
13
14
15
16
17
18
end
Deactivate previous agent action
if agent = central character then
Retrieve eye-tracking coordinates
Check if in character bounds
if In character bounds then
Pick agent action in attentive pool
else
Pick agent action in distracted pool
else
Pick new agent action
3.4 Results
Figure 3.1 depicts the results obtained from the combination of our environments with our
characters. As mentioned in Section 3.2, we have created various scenarios by positioning
the user and the characters in different positions in the different environments. Regarding the
use of eye-tracking for interaction, Figure 3.4 illustrates the difference in behavior between
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