Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks

Skublewska‐Paszkowska, Maria; Powroźnik, Paweł; Lukasik, Edyta

doi:10.3390/s20216094

Cited by 18 publications

(11 citation statements)

References 31 publications

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“…The peak anaerobic power of adult tennis players is 10–12 W/kg (men) and 8–10 W/kg (women), which are weaker than those with higher exercise intensity. The tennis incremental run stroke test appears to be a simple and effective method for evaluating V.O2max in tennis players [ 22 ].…”

Section: Resultsmentioning

confidence: 99%

Biomechanical Analysis of Touch Ball Movements in Tennis Forehand Strokes

Luo

2022

Computational Intelligence and Neuroscience

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In order to improve the effect of certain theoretical basis for tennis coaches to correct technical movements and teaching training, a method oriented towards discrete gradient methods that can be used for computational solid mechanics is presented. The biggest feature of this method is that it can directly perform numerical simulation analysis on any point cloud model, without relying on any structured or unstructured grid model. Experimental results show that about 80% of the shots in the game are within 2.5 m of the athlete’s moving distance, and the athlete needs to have 300 to 500 high-intensity exercises; the total running distance of the competition is 1 100–3 600 m. The average VO2 of athletes during the competition is 20–30 mL/min/kg (45%–55% V.O2max), the average heart rate is 135–155 beats/min (70%–85% HRmax), the mean blood lactate was <4 mmol/L, the subjective fatigue was 12–14 (moderate intensity), and the mean metabolic equivalent was 5–7 METs. It is proved that the discrete gradient method can effectively solve the biomechanical analysis problem of tennis forehand hitting the ball. Make up for the lack of action details in the differentiation stage. Improve the effect of certain theoretical basis for tennis coaches to correct technical movements and teaching training.

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Section: Resultsmentioning

confidence: 99%

Biomechanical Analysis of Touch Ball Movements in Tennis Forehand Strokes

Luo

2022

Computational Intelligence and Neuroscience

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“…of the DMMv do not have too much noise interference, and the features extracted from the model are enough to distinguish most behaviors [19].…”

Section: E Influence Of Integration Decision On Identificationmentioning

confidence: 99%

Research on Tennis Motion Evaluation Method and System Based on Deep Learning

Chang

2022

International Transactions on Electrical Energy Systems

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The current tennis training lacks the guidance to standardize the user action evaluation method; it is difficult to use the human movement analysis related technology to evaluate the user action and feedback. In this paper, a real-time evaluation algorithm for human movements in tennis training with the monocular camera is studied. Aiming at solving the problem of making use of temporal and spatial information of behavior sequence, an improved preprocessing algorithm based on depth image and bone data was proposed, and bone joint feature vectors were constructed to describe the topological shape of the human skeleton. A five-channel convolutional neural network (5C-CNN) model is proposed to effectively train multimodal behavior data. In order to solve the problem of human motion direction, the counterclockwise rotation angle of limbs was proposed as a motion evaluation feature. The experimental test on the MSR Action 3D database shows that the accuracy of the method for tennis movement evaluation reaches 98.1%, which verifies the validity of the model construction.

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“…Although a number of technologies have been proposed to get motion data in racket sports, including 3D optical systems based on retroflective markers captured by multiple cameras [ 38 ], and Inertial Measurement Units (IMUs) [ 39 , 40 , 41 ], these approaches require players to wear the IMUs/markers and thus cannot be applied to analyze already existing videos. The marker-less, video-based techniques we compared in this paper fill this gap by allowing the analysis of elite and amateur videos captured in noncontrolled setups.…”

Section: Discussionmentioning

confidence: 99%

Estimating Player Positions from Padel High-Angle Videos: Accuracy Comparison of Recent Computer Vision Methods

Javadiha

Andújar

Lacasa

et al. 2021

Sensors

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The estimation of player positions is key for performance analysis in sport. In this paper, we focus on image-based, single-angle, player position estimation in padel. Unlike tennis, the primary camera view in professional padel videos follows a de facto standard, consisting of a high-angle shot at about 7.6 m above the court floor. This camera angle reduces the occlusion impact of the mesh that stands over the glass walls, and offers a convenient view for judging the depth of the ball and the player positions and poses. We evaluate and compare the accuracy of state-of-the-art computer vision methods on a large set of images from both amateur videos and publicly available videos from the major international padel circuit. The methods we analyze include object detection, image segmentation and pose estimation techniques, all of them based on deep convolutional neural networks. We report accuracy and average precision with respect to manually-annotated video frames. The best results are obtained by top-down pose estimation methods, which offer a detection rate of 99.8% and a RMSE below 5 and 12 cm for horizontal/vertical court-space coordinates (deviations from predicted and ground-truth player positions). These results demonstrate the suitability of pose estimation methods based on deep convolutional neural networks for estimating player positions from single-angle padel videos. Immediate applications of this work include the player and team analysis of the large collection of publicly available videos from international circuits, as well as an inexpensive method to get player positional data in amateur padel clubs.

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Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks

Cited by 18 publications

References 31 publications

Biomechanical Analysis of Touch Ball Movements in Tennis Forehand Strokes

Biomechanical Analysis of Touch Ball Movements in Tennis Forehand Strokes

Research on Tennis Motion Evaluation Method and System Based on Deep Learning

Estimating Player Positions from Padel High-Angle Videos: Accuracy Comparison of Recent Computer Vision Methods

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