Abstract
In this study, we analyzed the blinking behavior of players in a
video game tournament. Our aim was to test whether spontaneous
blink patterns differ across levels of expertise. We used blink rate,
blink duration, blink frequency, and eyelid movements represented
by the Eye Aspect Ratio (EAR) to train a machine learning classifier to discriminate between different levels of expertise. Classifier
performance was highly influenced by features such as the mean,
standard deviation and median EAR. Moreover, further analysis
suggests that blinking rate and blink duration are likely to increase
along with the level of expertise. We speculate this may be indicative of a reduction in cognitive load and lowered stress of expert
players. In general, our results suggest that EAR and blink patterns
can be used to identify different levels of expertise of video game
players.
video game tournament. Our aim was to test whether spontaneous
blink patterns differ across levels of expertise. We used blink rate,
blink duration, blink frequency, and eyelid movements represented
by the Eye Aspect Ratio (EAR) to train a machine learning classifier to discriminate between different levels of expertise. Classifier
performance was highly influenced by features such as the mean,
standard deviation and median EAR. Moreover, further analysis
suggests that blinking rate and blink duration are likely to increase
along with the level of expertise. We speculate this may be indicative of a reduction in cognitive load and lowered stress of expert
players. In general, our results suggest that EAR and blink patterns
can be used to identify different levels of expertise of video game
players.
| Original language | English |
|---|---|
| Title of host publication | 17th International Conference on the Foundations of Digital Games (FDG) |
| Place of Publication | Athens |
| Publisher | ACM |
| Edition | 17th |
| Publication status | Accepted/In press - 21 May 2022 |