Effects of Acute Physical Fatigue on Gaze Behavior and Performance During a Badminton Game

Abstract

In badminton, the ability to quickly gather relevant visual information is one of the most important determinants of performance. However, gaze behavior has never been investigated in a real-game setting (with fatigue), nor related to performance. The aim of this study was to evaluate the effect of fatigue on gaze behavior during a badminton game setting, and to determine the relationship between fatigue, performance and gaze behavior. Nineteen novice badminton players equipped with eye-tracking glasses played two badminton sets: one before and one after a fatiguing task. The duration and number of fixations for each exchange were evaluated for nine areas of interest. Performance in terms of points won or lost and successful strokes was not impacted by fatigue, however fatigue induced more fixations per exchange on two areas of interest (shuttlecock and empty area after the opponent's stroke). Furthermore, two distinct gaze behaviors were found for successful and unsuccessful performance: points won were associated with fixations on the boundary lines and few fixation durations on empty area before the participant's stroke; successful strokes were related to long fixation durations, few fixation durations on empty area and a large number of fixations on the shuttlecock, racket, opponent's upper body and anticipation area. This is the first study to use a mobile eye-tracking system to capture gaze behavior during a real badminton game setting: fatigue induced changes in gaze behavior, and successful and unsuccessful performance were associated with two distinct gaze behaviors.

Eye-tracking technology and the dynamics of natural gaze behavior in sports: an update 2016–2022

“…Some studies (e.g., Sáenz-Moncaleano et al, 2018 ) seem to employ algorithmic event-detection algorithms (e.g., for fixations) first, and assign AOIs to events in a second step (leading to a significant reduction of manual work), whilst others (e.g., Van Maarseveen et al, 2018 ) perform a frame-by-frame GCA and classify fixations downstream based on these categorial allocations, obviously omitting detailed spatial characteristics of gaze locations. Some ignore these – perceptually relevant – event detections altogether or even redefine standard definitions (e.g., Loiseau-Taupin et al, 2021 ). Nevertheless, decent progress can be noticed concerning an algorithmic rather than a manual approach to GCA as – compared to the 8.3% reported by Kredel et al (2017) – an algorithmic approach has been pursued in 16.1% of studies over more recent years.…”