Visual Fixations Duration as an Indicator of Skill Level in eSports

Velichkovsky, Boris B.; Khromov, Nikita; Korotin, Alexander; Burnaev, Evgeny; Somov, Andrey

doi:10.1007/978-3-030-29381-9_25

Cited by 21 publications

(12 citation statements)

References 13 publications

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“…The distribution of fixation durations is quite predictable, famously non-normal and characteristically skewed to the left with typical median durations of 200-250 ms, mean durations of 300-350 ms, and an extended right tail of long and very long fixations (Rayner, 1998;Unema, Pannasch, Joos, & Velichkovsky, 2005;Velichkovsky et al, 2019) (see Fig. 1).…”

Section: Classification Of Fixation Typesmentioning

confidence: 99%

“…A different approach in ambient-focal classification is based only on fixation durations without considering saccade characteristics at all (Velichkovsky, 1999). Such classification suggests short fixations (50-150 ms) to be of ambient type while long fixations around 300-500 ms are considered to be focal (Velichkovsky et al, 2019). These two populations of fixations, ambient and focal, with small differences in the actual ranges used by researchers (see Table 1), have often been reported in eye-movement research.…”

Section: Classification Of Fixation Typesmentioning

confidence: 99%

“…The fixation durations in an eye tracking dataset vary from as small as 50 ms to several seconds (over 2000 ms) (Buswall, 1935;Velichkovsky et al, 2019). The aforementioned ambient/focal classification of fixations does not adequately represent this wide range.…”

Section: Classification Of Fixation Typesmentioning

confidence: 99%

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Fixation duration and the learning process: an eye tracking study with subtitled videos

Negi

Mitra

2020

JEMR

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Learning is a complex phenomenon and education researchers are increasingly focussing on processes that go into it. Eye tracking has become an important tool in such research. In this paper, we focus on one of the most commonly used metrics in eye tracking, namely, fixation duration. Fixation duration has been used to study cognition and attention. However, fixation duration distributions are characteristically non-normal and heavily skewed to the right. Therefore, the use of a single average value, such as the mean fixation duration, to predict cognition and/or attention could be problematic. This is especially true in studies of complex constructs, such as learning, which are governed by both cognitive and affective processes. We collected eye tracking data from 51 students watching a 12 min long educational video with and without subtitles. The learning gain after watching the video was calculated with pre- and post-test scores. Several multiple linear regression models revealed a) fixation duration can explain a substantial fraction of variation in the pre-post data, which indicates its usefulness in the study of learning processes; b) the arithmetic mean of fixation durations, which is the most commonly reported eye tracking metric, may not be the optimal choice; and c) a phenomenological model of fixation durations where the number of fixations over different temporal ranges are used as inputs seemed to perform the best. The results and their implications for learning process research are discussed.

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Section: Classification Of Fixation Typesmentioning

confidence: 99%

Section: Classification Of Fixation Typesmentioning

confidence: 99%

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Fixation duration and the learning process: an eye tracking study with subtitled videos

Negi

Mitra

2020

JEMR

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“…Count Buswell (1935), Yarbus (1967), Schoonahd et al (1973), Brutten and Janssen (1979), Megaw and Richardson (1979), Megaw (1979), Goldberg and Kotval (1999), Coeckelbergh et al (2002), Jacob and Karn (2003), Ares et al (2013) Duration Young andSheena (1975), Rayner (1978), Rayner (1979), Salthouse and Ellis (1980), Karsh and Breitenbach (1983), Goldberg and Kotval (1999), Velichkovsky et al (2000), Pavlović and Jensen (2009), Staub and Benatar (2013), Ares et al (2013), Reingold et al (2012), Menon et al (2016), Costa et al (2018), Henderson et al (2018), Velichkovsky et al (2019) SACCADE Related Work Amplitude Bahill et al (1975), Ceder (1977), Rayner (1978), Megaw and Richardson (1979), May et al (1990), Zelinsky and Sheinberg (1997), Phillips and Edelman (2008) Becker and Fuchs (1969), Lehtinen et al (1979), Griffiths et al (1984), Abel and Hertle (1988), Galley (1993), McGregor and Stern (1996), Castello et al (1998), Russo et al (2003) Barmack...…”

Section: Fixation Related Workmentioning

confidence: 99%

Eye Movement and Pupil Measures: A Review

Mahanama

Jayawardana

Rengarajan

et al. 2022

Front. Comput. Sci.

110

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Our subjective visual experiences involve complex interaction between our eyes, our brain, and the surrounding world. It gives us the sense of sight, color, stereopsis, distance, pattern recognition, motor coordination, and more. The increasing ubiquity of gaze-aware technology brings with it the ability to track gaze and pupil measures with varying degrees of fidelity. With this in mind, a review that considers the various gaze measures becomes increasingly relevant, especially considering our ability to make sense of these signals given different spatio-temporal sampling capacities. In this paper, we selectively review prior work on eye movements and pupil measures. We first describe the main oculomotor events studied in the literature, and their characteristics exploited by different measures. Next, we review various eye movement and pupil measures from prior literature. Finally, we discuss our observations based on applications of these measures, the benefits and practical challenges involving these measures, and our recommendations on future eye-tracking research directions.

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“…Authors in [3] propose to use data from eye tracker to describe traits of professional players in terms of visual fixations, or saccades. They argue that pros have more variable fixation durations than amateur players.…”

Section: A Sensor Data In Esports Researchmentioning

confidence: 99%

Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning

Smerdov,

Somov,

Burnaev

et al. 2020

Preprint

Self Cite

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Current research in eSports lacks the tools for proper game practising and performance analytics. The majority of prior work relied only on in-game data for advising the players on how to perform better. However, in-game mechanics and trends are frequently changed by new patches limiting the lifespan of the models trained exclusively on the in-game logs. In this article, we propose the methods based on the sensor data analysis for predicting whether a player will win the future encounter. The sensor data were collected from 10 participants in 22 matches in League of Legends video game. We have trained machine learning models including Transformer and Gated Recurrent Unit to predict whether the player wins the encounter taking place after some fixed time in the future. For 10 seconds forecasting horizon Transformer neural network architecture achieves ROC AUC score 0.706. This model is further developed into the detector capable of predicting that a player will lose the encounter occurring in 10 seconds in 88.3% of cases with 73.5% accuracy. This might be used as a players' burnout or fatigue detector, advising players to retreat. We have also investigated which physiological features affect the chance to win or lose the next in-game encounter.

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Visual Fixations Duration as an Indicator of Skill Level in eSports

Cited by 21 publications

References 13 publications

Fixation duration and the learning process: an eye tracking study with subtitled videos

Fixation duration and the learning process: an eye tracking study with subtitled videos

Eye Movement and Pupil Measures: A Review

Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning

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