[HTML][HTML] Deep learning neural network-assisted badminton movement recognition and physical fitness training optimization

C He, M Zhang - Heliyon, 2024 - cell.com
This work aims to solve the problem of low accuracy in recognizing the trajectory of
badminton movement. This work focuses on the visual system in badminton robots and …

[HTML][HTML] Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks

S Tan, TT Teoh - Applied Sciences, 2024 - mdpi.com
This paper presents a novel approach to predicting shot accuracy in badminton by analyzing
Quiet Eye (QE) metrics such as QE duration, fixation points, and gaze dynamics. We develop …

Automatic Shuttlecock Motion Recognition Using Deep Learning

Y Zhao - IEEE Access, 2023 - ieeexplore.ieee.org
In the field of sports video processing, specifically in the context of motion recognition for
shuttlecock match videos, we first propose a method based on attitude estimation to …

Time Series Classification of Badminton Pose using LSTM with Landmark Tracking

B Purnama, B Erfianto, IR Wirawan - Journal of Electronics …, 2025 - jeeemi.org
Traditional methods of analyzing badminton matches, such as video movement analysis, are
time-consuming, prone to errors, and rely heavily on manual annotation. This creates …

Badminton Service Foul System based on machine vision

C Zhenyang, F Caluyo, AL De Ocampo… - Salud, Ciencia y …, 2024 - dialnet.unirioja.es
Introduction: In today's sports activity landscape, the identity of fouls and misguided moves in
badminton poses extensive challenges. A badminton carrier foul takes place when a player …