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      Biomechanical Analysis of Touch Ball Movements in Tennis Forehand Strokes

      research-article
      Computational Intelligence and Neuroscience
      Hindawi

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          Abstract

          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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          Most cited references25

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          Getting your game on: Using virtual reality to improve real table tennis skills

          Objective The present study investigates skill transfer from Virtual Reality (VR) sports training to the real world, using the fast-paced sport of table tennis. Background A key assumption of VR training is that the learned skills and experiences transfer to the real world. Yet, in certain application areas, such as VR sports training, the research testing this assumption is sparse. Design Real-world table tennis performance was assessed using a mixed-model analysis of variance. The analysis comprised a between-subjects (VR training group vs control group) and a within-subjects (pre- and post-training) factor. Method Fifty-seven participants (23 females) were either assigned to a VR training group (n = 29) or no-training control group (n = 28). During VR training, participants were immersed in competitive table tennis matches against an artificial intelligence opponent. An expert table tennis coach evaluated participants on real-world table tennis playing before and after the training phase. Blinded regarding participant's group assignment, the expert assessed participants’ backhand, forehand and serving on quantitative aspects (e.g. count of rallies without errors) and quality of skill aspects (e.g. technique and consistency). Results VR training significantly improved participants’ real-world table tennis performance compared to a no-training control group in both quantitative (p < .001, Cohen’s d = 1.08) and quality of skill assessments (p < .001, Cohen’s d = 1.10). Conclusions This study adds to a sparse yet expanding literature, demonstrating real-world skill transfer from Virtual Reality in an athletic task.
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            Comparative Study of Table Tennis Forehand Strokes Classification Using Deep Learning and SVM

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              Kinematic Parameters of Topspin Forehand in Table Tennis and Their Inter- and Intra-Individual Variability.

              The aims of the research were to (1) determine the values of kinematic parameters in two modifications of the topspin forehand stroke as well as the differences between them and (2) assess the inter-individual and intra-individual variability of the values. Two modifications of a topspin forehand were evaluated: topspin after a topspin ball (TF1) and topspin after a backspin ball (TF2). The MyoMotion Noraxon analysis system was used to record the kinematic data. A piezo-electric sensor was used to identify the moment when the ball made contact with the racket. The coefficient of variation determined the variability of the kinematic parameters. Most of the joint angles in four identified events reflected how the individual segments of a player's body should move. The difference in acceleration at the moment of contact between the two types of the topspin forehand was significant, but the variability of the acceleration values was small. Large variability in the angular parameters was found, and this result was considered a manifestation of different coordination patterns in the stroke movements. It is possible that even though the players used different methods of performing the movement, they obtained similar values for some parameters (e.g., acceleration), which should be taken into account by coaches. There were small differences in many parameters within individual players, which can indicate that a player performs tasks in a similar way each time. However, there was high variability in some angular parameters, indicating that the repetitions of particular strokes were not performed in an identical way. The reasons for this phenomenon include movement functionality and functional variability.
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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2022
                16 June 2022
                : 2022
                : 5754820
                Affiliations
                Department of Physical Education, Guangdong Pharmaceutical University, Guangzhou, Guangdong 510006, China
                Author notes

                Academic Editor: Hongru Zhao

                Author information
                https://orcid.org/0000-0003-2298-0452
                Article
                10.1155/2022/5754820
                9225835
                35755726
                d3b24096-8c84-4b05-bcf2-40b23953c809
                Copyright © 2022 Wufeng Luo.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 April 2022
                : 25 May 2022
                : 4 June 2022
                Categories
                Research Article

                Neurosciences
                Neurosciences

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