Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioning

LUO, J, HU, Y, DAVIDS, Keith, ZHANG, D, GOUIN, C, LI, X and XU, X (2022). Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioning. Heliyon, 8: e10089.

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Abstract

Coordinating dynamic interceptive actions in sports like badminton requires skilled performance in getting the racket into the right place at the right time. For this reason, the strategic movement and placement of one's feet, or footwork, is an important part of competitive performance. Developing an automated, efficient, and economical method to record individual movement characteristics of players is critical and can benefit athletes and motor control specialists. Here, we propose new methods for recording data on the footwork of individual badminton players, in which deep learning is used to obtain image coordinates (2D) of their shoes and binocular positioning to reconstruct the 3D coordinates of the shoes. Results show that the final positioning accuracy is 74.7%. Using the proposed methods, we revealed inter-individual adaptations in the footwork of several participants during competitive performance. The data provided insights on how individual participants coordinated footwork to intercept the projectile, by varying the distance traveled on court and jump height. Compared with visual observations by biomechanists and motor control specialists, the proposed methods can obtain quantitative data, provide analysis and evaluation of each participant's performance, revealing personal characteristics that could be targeted to shape the individualized training programs of players to refine their badminton footwork.

Item Type:	Article
Identification Number:	https://doi.org/10.1016/j.heliyon.2022.e10089
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	08 Sep 2022 10:05
Last Modified:	12 Oct 2023 08:03
URI:	https://shura.shu.ac.uk/id/eprint/30683

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