Careful what you say to yourself: Exploring self-talk and youth tennis performance via hierarchical linear modeling

Thibodeaux, Jordan; Winsler, Adam

doi:10.1016/j.psychsport.2020.101646

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…A study assessing the motivational self-talk (MST) in endurance athletes showed improvement in time to exhaustion (TTE) in group which performed MST training before their performance Pre (487 ± 173s) compared with their significantly better performance Post MST training (679 ± 251s) (p = 0.021), whereas the control group (CON) scored insignificantly worse results during their second trial Pre (531 ± 178s) and Post (510 ± 216s) (p = 0.28), (Wallace et al, 2017). This study proves that self-talk can improve attentional focus and maintain attentional focus under distraction and fatigue (Thibodeaux & Winsler, 2020). The use of self-talk can improve jump-off reaction from the starting block and overall improvement in kinesthetic skills (Boroujeni & Ghaheri, 2011).…”

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confidence: 56%

“…To test for a self-talk effect in no-pressure and under-pressure situations, a heart rate monitor was used to test for differences in each self-talk group (Liu et al, 2022). Thibodeaux & Winsler (2020) used an observational method during the participants' tennis matches and after their matches, they recorded their performance and speech using a specialized scale to assess the effect of self-talk on their performance.…”

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confidence: 99%

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Self-talk and its application to competitive swimming

Zach

2024

Studia Kinanthropologica

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Competitive endurance swimming is a discipline that requires full focus and concentration to achieve peak performance. Athletes use various psychological strategies to achieve their full concentration. One of these strategies is self-talk, which has gained more popularity in recent years. Self-talk may be divided such as internal and external self-talk. Long-term self-talk is proven to be the most effective compared with short-term self-talk. Competitive swimmers can create their scripts with their selection of self-talk cues that they have evaluated with their coach as those that need to be improved. Self-talk can help competitive swimmers improve their starting reactions, improve their kinesthetic skills, muscular strength, endurance, and kinematics. The use of self-talk may enhance athletic performance through the improvement in learning achievements, improved athletic skills, and self-confidence.

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Self-talk and its application to competitive swimming

Zach

2024

Studia Kinanthropologica

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“…After the video image is pre-processed, the open-source library OpenPose of human body gesture recognition project [17] is applied to obtain the information of human skeleton joints from the image, and it can generally obtain 18 joint points from a complete front image. As shown in Figure 1, 0 -17 mean nose, neck, right shoulder, right elbow, right wrist, left shoulder, left elbow, left wrist, right hip, right knee, right ankle, left hip, left knee, left ankle, right eye, left eye, right ear, and left ear in return, respectively.…”

Section: B Construction Of the Cnn-based Real-time Evaluation Algorit...mentioning

confidence: 99%

The Biomechanical Analysis on the Tennis Batting Angle Selection Under Deep Learning

Li,

Zhang,

Yang

2023

IEEE Access

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The objective is to study the impacts of batting strength and angle of tennis players on batting results based on deep learning (DL) image processing technology. A real-time evaluation algorithm of human motion is constructed based on the camera video image and convolution neural network (CNN), and the selection of joint angles in volley training of tennis players is analyzed from the perspective of biomechanics. Gaussian Mixture Model (GMM), Visual Background Extractor (VIBE), and Optical Flow (OF) are introduced for simulation and comparison. Then, the proposed algorithm is applied to the volley experiments in areas A, B, and C of 6 tennis players (denoted by P1, P2, P3, P4, P5, and P6). The results show that the processing frame rate and batting and follow-up similarity score of the proposed algorithm based on the camera video image and CNN are significantly higher than those of GMM, VIBE, and OF. The return success rates of P1 in different areas are the highest, which are 75.46%, 75.62%, and 68.94%, respectively; while those of P6 are the lowest (19.55%, 17.46%, and 21.65%, respectively). The left ankle angle of P6 is much greater than that of P1, the angle of P1 is significantly lower than that of P3, P4, P5, and P6. The batting speed of P1 is significantly slower than that of P3, P4, P5, and P6, which is not much different from that of the left knee joint. The angles of the subjects' right forearm ring, left lower leg ring, and left thigh ring is obvious. Additionally, the displacement of the left foot of P1 and P6 in area A is 0.916m and 0.548m, respectively. Therefore, in the volley preparation stage, the left ankle angle (103-108°) is greater than that of the right ankle (98-103°); the tennis batting speed should be basically the same as that of the left knee joint to lower the gravity center of player. Thus, the proposed algorithm outperforms other algorithms in the volley experiment of tennis players.INDEX TERMS Convolutional neural networks; monocular camera video image; real-time evaluation algorithm of human motion; volley experiment of tennis; biomechanics

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