Abstract
This study aimed to evaluate if eye blinks can be used to discriminate players with different performance in a session of Nintendo Entertainment System (NES) Tetris. To that end, we developed a state-of-the-art method for blink extraction from EAR measures, which is robust enough to be used with data collected by a low-grade webcam such as the ones widely available on laptop computers. Our results show a significant decrease in blink rate per minute (blinks/m) during the first minute of playing Tetris. After having defined 3 groups of proficiency based on in-game performance (Novices, Intermediates, and Experts) we found out that expert players display a significantly lower decrease in blinks/m compared to novices during the first minute of gameplay, which shows that Tetris players' proficiency can be detected by looking at eye blinks/m variations during the early phase of a game session. This difference in blinks/m is observed throughout the entire game session, which supports the general conclusion that proficient Tetris players have a lower decrease in blinks/m, even when playing more difficult levels.
Original language | English |
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Pages (from-to) | 735 - 741 |
Number of pages | 7 |
Journal | IEEE Transactions on Games |
Volume | 16 |
Issue number | 3 |
DOIs | |
Publication status | Accepted/In press - 2023 |
Keywords
- Ear
- Expertise
- Eye blinks
- Faces
- Filtering
- Forestry
- Games
- Machine learning
- Performance
- Recording
- Video games