• Detecting optimal gaze behavio...

Treffer: Detecting optimal gaze behavior of successful basketball free throwing via machine learning system.

Title:
Detecting optimal gaze behavior of successful basketball free throwing via machine learning system.
Authors:
Asadi A; Department of Motor Behavior, Faculty of Sport Sciences, Alzahra University, Tehran, Iran; Department of Kinesiology, Iowa State University, Ames, IA, USA. Electronic address: asadi68@iastate.edu., Daneshfar A; Department of Motor Behavior, Faculty of Sport Sciences, Alzahra University, Tehran, Iran., Saeedpour-Parizi MR; Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, USA., Aiken CA; Department of Kinesiology, New Mexico State University, USA., Smiley A; Department of Kinesiology, Iowa State University, Ames, IA, USA.
Source:
Human movement science [Hum Mov Sci] 2025 Aug; Vol. 102, pp. 103381. Date of Electronic Publication: 2025 Jun 13.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: North-Holland Pub. Co Country of Publication: Netherlands NLM ID: 8300127 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7646 (Electronic) Linking ISSN: 01679457 NLM ISO Abbreviation: Hum Mov Sci Subsets: MEDLINE
Imprint Name(s):
Original Publication: Amsterdam [Netherlands] : North-Holland Pub. Co., [1982-
MeSH Terms:
Basketball*/physiology , Machine Learning* , Eye-Tracking Technology* , Fixation, Ocular*/physiology , Athletic Performance*/physiology , Motor Skills*/physiology , Psychomotor Performance*/physiology, Humans ; Male ; Young Adult ; Saccades/physiology ; Female ; Adolescent
Contributed Indexing:
Keywords: Basketball; Fixations; Microsaccades; Quiet eye; Saccades
Entry Date(s):
Date Created: 20250614 Date Completed: 20250809 Latest Revision: 20250809
Update Code:
20250811
DOI:
10.1016/j.humov.2025.103381
PMID:
40516144
Database:
MEDLINE

Weitere Informationen

Eye tracking in sport is an emerging field that explores the relationships between visual function and motor performance. However, research has shown that visual behaviors are distinct enough to detect superior performance; and serve as a suitable input for designing machine learning systems, few study has been tested yet the eye tracking machine learning in sport tasks. The current research investigated the eye movement behaviors for detecting successful performance using machine learning. The gaze behavior of 25 student basketball players during the hit and miss free- throwing's trials was collected and analyzed by statistical (JMP pro) and machine learning (Python) approaches. Results showed significant differences between saccade duration in hit and miss trials. In previous studies of free throwing, fixations were used as a measure of visual information processing, but our results showed that the metrics related to saccades were more important for successful performance than those related to fixations. These findings highlight the importance of eye tracking machine learning in sport domain and suggest that successful performance can be reliably predicted from performers' eye movement data. Our results provide primary insights as well as inspiration for future research focusing on developing eye-tracking machine learning systems to detect proficiency in motor skills.
(Copyright © 2025 Elsevier B.V. All rights reserved.)

Declaration of Competing Interest The authors declare that the study was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.