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

Luo, Jiabei; Hu, Yujie; Davids, Keith; Zhang, Di; Gouin, Cade; Li, Xiang; Xu, Xiangde

doi:10.1016/j.heliyon.2022.e10089

Cited by 15 publications

(5 citation statements)

References 42 publications

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“…Every position that becomes difficult for athletes' footwork is the backhand position because it moves towards the back corner of the opponent's fulcrum, that is, the backhand. Players must step as little as possible in the direction in which the shuttlecock comes to intercept the direction of the shuttlecock to deliver a blow in the desired direction (Luo et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

The Effectiveness of Hurdle Drill and Shadow Exercises to Improve Footwork Skills in Badminton

Kurniawan,

Suherman,

Mulyanto

2024

J. Ilm. Pendidik. Jasm

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This research shows the importance of this type of exercise in badminton to improve footwork skills. The research aimed to determine the effect of hurdle drills and shadow training on footwork and which exercises are the most effective. This research uses a quantitative approach method with a quasi-experimental design. The sample involved in this study consisted of 30 PB athletes. Sampurna Sumedang, aged 10-14, was selected using purposive sampling techniques. This study involved three groups; One control group was a conventional exercise, and two experimental groups were doing hurdle drills and shadow exercises. Pretest and posttest data were obtained using Tohar's badminton footwork research instrument. Data processing and analysis using ANAVA: Sig. value 0.03 < α = 0.05, meaning significant differences from each group to footwork. The post hoc test showed the results of a hurdle drill with a value of 15.30, shadow training with a result of 14.70, and a control group with a value of 13.60. In conclusion, hurdle drill training, shadow training, and conventional training are effective in badminton footwork. The results of this study show that hurdle drill exercises are better than shadow exercises and conventional exercises because they are more varied and efficient.

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Section: Discussionmentioning

confidence: 99%

The Effectiveness of Hurdle Drill and Shadow Exercises to Improve Footwork Skills in Badminton

Kurniawan,

Suherman,

Mulyanto

2024

J. Ilm. Pendidik. Jasm

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“…On the Jeston Nano hardware, the GoogleNet model outperformed, achieving 83.04% and 97.0% accuracy on training and testing, respectively. In one study ([ 23 ]), a new approach was utilized to collect data on the footwork of badminton players. This study used a deep-learning method to extract two-dimensional (2D) and 3D coordinates of the players’ shoes.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning

Siddiqui,

Younas,

Rustam

et al. 2023

Sensors

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Cricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes eight strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep. The study uses the MediaPipe library to extract features from videos and several machine learning and deep learning algorithms, including random forest (RF), support vector machine, k-nearest neighbors, decision tree, linear regression, and long short-term memory to predict the strokes. The study achieves an outstanding accuracy of 99.77% using the RF algorithm, outperforming the other algorithms used in the study. The k-fold validation of the RF model is 95.0% with a standard deviation of 0.07, highlighting the potential of computer vision and machine learning techniques for predicting batsman strokes in cricket. The study’s results could help improve coaching techniques and enhance batsmen’s performance in cricket, ultimately improving the game’s overall quality.

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“…In the shot-sequence, the recognition object, i.e., the main player, occupies a partial region in each frame and exhibits spatial locality. CBAM (Convolutional Block Attention Module) [18], including spatial attention mechanism and channel attention mechanism, can enhance the generalization performance of network by focusing on important features and suppressing irrelevant features. It can be embedded into different baseline ConvNet structures [19].…”

Section: Example Of Optical Flowmentioning

confidence: 99%

Automatic Shuttlecock Motion Recognition Using Deep Learning

Zhao

2023

IEEE Access

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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 measure the movement extent of the shotting arm, allowing for temporal positioning of shuttlecock shot motions and extraction of corresponding shot-sequences. The shot-sequences is defined in this paper as video segments that exclusively contain the complete shot motion of the mainplayer. During the training phase, we incorporate a lightweight channel-spatial attention mechanism into the Temporal Segment Network (TSN) to classify the extracted shot-sequences into four types, i.e., forehand shot, backhand shot, smash shot, and drop shot. Furthermore, we employ image morphology-based techniques to further distinguish smash shot-sequences as either high clear shots or kill shots based on the shuttlecock's motion trajectory. The extensive experiment results demonstrate the effectiveness of proposed method in accurately positioning and recognizing shuttlecock shot motions.

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

Cited by 15 publications

References 42 publications

The Effectiveness of Hurdle Drill and Shadow Exercises to Improve Footwork Skills in Badminton

The Effectiveness of Hurdle Drill and Shadow Exercises to Improve Footwork Skills in Badminton

Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning

Automatic Shuttlecock Motion Recognition Using Deep Learning

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