Tracking Early Differences in Tetris Performance using Eye Aspect Ratio Extracted Blinks

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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 languageEnglish
Pages (from-to)735 - 741
Number of pages7
JournalIEEE Transactions on Games
Volume16
Issue number3
DOIs
Publication statusAccepted/In press - 2023

Keywords

  • Ear
  • Expertise
  • Eye blinks
  • Faces
  • Filtering
  • Forestry
  • Games
  • Machine learning
  • Performance
  • Recording
  • Video games

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