scholarly journals Skill Level in Tennis Serve Return Is Related to Adaptability in Visual Search Behavior

2021 ◽  
Vol 12 ◽  
Author(s):  
Jernej Rosker ◽  
Ziva Majcen Rosker
Keyword(s):  
Visual Search ◽  
Hand Movement ◽  
National Level ◽  
Area Under The Curve ◽  
Search Strategies ◽  
Visual Behavior ◽  
Tennis Players ◽  
Machine Learning Model ◽  
Tennis Serve ◽  
The Individual

Analyzing visual search strategies in tennis is primarily focused on studying relationships between visual behavior and tennis performance. However, diverse movement characteristics among different servers suggest the importance of adjusting the visual search strategies of an individual while playing against different opponents. The aim of this study was to analyze whether visual search strategies can be attributed to the individual server and the returning player during the tennis serve return or return performance. Seventeen tennis players were enrolled in this study (five international players and 12 national players) producing a sample of 1,020 returns measured with mobile eye trackers. The random forest machine learning model was used to analyze the ability to classify the returning player [area under the curve (AUC): 0.953], individual server (AUC: 0.686), and return performance category (AUC: 0.667) based on the location and duration of the focal vision fixation. In international tennis players, the higher predictability of the server was observed as compared with national level players (AUC: 0.901 and 0.834, respectively). More experienced tennis players presented with a higher ability to adjust their visual search strategies to different servers. International players also demonstrated anticipatory visual behavior during the tossing hand movement and superior information pickup during the final phases of the stroke of a server.

Anticipatory judgements associated with vision of an opponent’s end-effector: An approach by motion perturbation and spatial occlusion

2018 ◽  
Vol 72 (5) ◽  
pp. 1131-1140
Author(s):  
Hirofumi Ida ◽  
Kazunobu Fukuhara ◽  
Motonobu Ishii ◽  
Tetsuri Inoue
Keyword(s):  
Visual Analogue Scale ◽  
Visual Information ◽  
Analogue Scale ◽  
Rotation Speed ◽  
Elbow Extension ◽  
End Effector ◽  
Tennis Players ◽  
Occlusion Model ◽  
Tennis Serve ◽  
The Individual

This study was aimed at determining how the visual information of an end-effector (racket) and the intermediate extremity (arm) of a tennis server contribute to the receiver’s anticipatory judgement of ball direction. In all, 15 experienced tennis players and 15 novice counterparts viewed a spatially occluded computer graphics animation of a tennis serve (no-occlusion, racket-occlusion, and body-occlusion) and made anticipatory judgements of ball direction on a visual analogue scale (VAS). The patterns of the serve motions were generated by a simulation technique that computationally perturbs the rotation speed of the selected racket-arm joint (forearm pronation and elbow extension) on a captured serve motion. The results suggested that the anticipatory judgements were monotonically attuned with the perturbation rate of the forearm pronation speed excepting under the conditions of the racket-occlusion model. Although such attunements were not observed in the elbow perturbation conditions, the results of correlation analysis indicated that the residual information in the spatially occluded models had a similar effect to the no-occlusion model within the individual experienced participants. The findings support the notion that end-effector (racket) provides deterministic cues for anticipation, as well as imply that players are able to benefit from the relative motion of an intermediate extremity (elbow extension).

Uncertainty and Visual Strategies in Table Tennis

1989 ◽  
Vol 68 (2) ◽  
pp. 507-512 ◽  
Author(s):  
Hubert Ripoll
Keyword(s):  
Visual Search ◽  
Time Pressure ◽  
Motor Behavior ◽  
Search Pattern ◽  
Movement Preparation ◽  
Table Tennis ◽  
Visual Behavior ◽  
Tennis Players ◽  
Execution Phase ◽  
Visual Fixations

Ball games are characterized by perceptual uncertainty and time-pressure. Of interest is the visual-search pattern carried out when these characteristics change. This is the case in table-tennis situations when comparing a drill situation, when one kind of stroke is constantly repeated, and a match situation, when the stroke is more often unpredictable. We analysed, during play, the visual-search pattern of five expert table tennis players to examine the effect of uncertainty on visual behavior. Direction of gaze was recorded by a video-oculographic recorder (NAC Eye Mark Recorder IV). Analysis showed that (i) visual fixations towards opponent player were only systematic in a match, contrary to drill where they occurred less frequently. (ii) Whatever the situation, only the first part of the ball's trajectory was visually tracked. This occurred immediately after the opponent's release of the ball. Nevertheless, visual tracking was more frequent and of longer duration in a match. (iii) Analysis of motor behavior showed that the duration of the movement preparation was longer in a match while the duration of the execution phase was unchanged.

scholarly journals A multi-layer model for the early detection of COVID-19

2021 ◽  
Vol 18 (181) ◽  
pp. 20210284
Author(s):  
Erez Shmueli ◽  
Ronen Mansuri ◽  
Matan Porcilan ◽  
Tamar Amir ◽  
Lior Yosha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
General Health ◽  
Medical Condition ◽  
Area Under The Curve ◽  
Sociodemographic Characteristics ◽  
Layer Model ◽  
Machine Learning Model ◽  
Spatio Temporal ◽  
The Individual

Current COVID-19 screening efforts mainly rely on reported symptoms and the potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that uses four layers of information: (i) sociodemographic characteristics of the individual, (ii) spatio-temporal patterns of the disease, (iii) medical condition and general health consumption of the individual and (iv) information reported by the individual during the testing episode. We evaluated our model on 140 682 members of Maccabi Health Services who were tested for COVID-19 at least once between February and October 2020. These individuals underwent, in total, 264 516 COVID-19 PCR tests, out of which 16 512 were positive. Our multi-layer model obtained an area under the curve (AUC) of 81.6% when evaluated over all the individuals in the dataset, and an AUC of 72.8% when only individuals who did not report any symptom were included. Furthermore, considering only information collected before the testing episode—i.e. before the individual had the chance to report on any symptom—our model could reach a considerably high AUC of 79.5%. Our ability to predict early on the outcomes of COVID-19 tests is pivotal for breaking transmission chains, and can be used for a more efficient testing policy.

scholarly journals A Multilayer Model for Early Detection of COVID-19

2021 ◽  
Author(s):  
Erez Shmueli ◽  
Ronen Mansuri ◽  
Matan Porcilan ◽  
Tamar Amir ◽  
Lior Yosha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Medical Condition ◽  
Area Under The Curve ◽  
Spatiotemporal Patterns ◽  
Sociodemographic Characteristics ◽  
Multilayer Model ◽  
The Past ◽  
Machine Learning Model ◽  
The Individual

ABSTRACTCurrent efforts for COVID-19 screening mainly rely on reported symptoms and potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that utilizes four layers of information: 1) sociodemographic characteristics of the tested individual, 2) spatiotemporal patterns of the disease observed near the testing episode, 3) medical condition and general health consumption of the tested individual over the past five years, and 4) information reported by the tested individual during the testing episode. We evaluated our model on 140,682 members of Maccabi Health Services, tested for COVID-19 at least once between February and October 2020. These individuals had 264,516 COVID-19 PCR-tests, out of which 16,512 were found positive. Our multilayer model obtained an area under the curve (AUC) of 81.6% when tested over all individuals, and of 72.8% when tested over individuals who did not report any symptom. Furthermore, considering only information collected before the testing episode – that is, before the individual may had the chance to report on any symptom – our model could reach a considerably high AUC of 79.5%. Namely, most of the value contributed by the testing episode can be gained by earlier information. Our ability to predict early the outcomes of COVID-19 tests is pivotal for breaking transmission chains, and can be utilized for a more efficient testing policy.

scholarly journals Relationship Between Motor Variability, Accuracy, and Ball Speed in the Tennis Serve

2012 ◽  
Vol 33 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Ruperto Antúnez ◽  
Francisco Hernández ◽  
Juan García ◽  
Raúl Vaíllo ◽  
Jesús Arroyo
Keyword(s):  
Motion Tracking ◽  
Hand Movement ◽  
Linear Structure ◽  
Movement Variability ◽  
Motor Variability ◽  
Ball Speed ◽  
Tennis Players ◽  
Non Linear ◽  
Tennis Serve ◽  
Speed And Accuracy

Relationship Between Motor Variability, Accuracy, and Ball Speed in the Tennis ServeThe main objective of this study was to analyze the motor variability in the performance of the tennis serve and its relationship to performance outcome. Seventeen male tennis players took part in the research, and they performed 20 serves. Linear and non-linear variability during the hand movement was measured by 3D Motion Tracking. Ball speed was recorded with a sports radar gun and the ball bounces were video recorded to calculate accuracy. The results showed a relationship between the amount of variability and its non-linear structure found in performance of movement and the outcome of the serve. The study also found that movement predictability correlates with performance. An increase in the amount of movement variability could affect the tennis serve performance in a negative way by reducing speed and accuracy of the ball.

scholarly journals An 8-Stage Model for Evaluating the Tennis Serve

2011 ◽  
Vol 3 (6) ◽  
pp. 504-513 ◽  
Author(s):  
Mark Kovacs ◽  
Todd Ellenbecker
Keyword(s):  
Stage Model ◽  
Kinetic Chain ◽  
Descriptive Model ◽  
Tennis Players ◽  
Overhead Athletes ◽  
Specific Analysis ◽  
Depth Analysis ◽  
Tennis Serve ◽  
Total Body ◽  
The Individual

Background: The tennis serve is a complex stroke characterized by a series of segmental rotations involving the entire kinetic chain. Many overhead athletes use a basic 6-stage throwing model; however, the tennis serve does provide some differences. Evidence Acquisition: To support the present 8-stage descriptive model, data were gathered from PubMed and SPORTDiscus databases using keywords tennis and serve for publications between 1980 and 2010. Results: An 8-stage model of analysis for the tennis serve that includes 3 distinct phases—preparation, acceleration, and follow-through—provides a more tennis-specific analysis than that previously presented in the clinical tennis literature. When a serve is evaluated, the total body perspective is just as important as the individual segments alone. Conclusion: The 8-stage model provides a more in-depth analysis that should be utilized in all tennis players to help better understand areas of weakness, potential areas of injury, as well as components that can be improved for greater performance.

scholarly journals Pro-Environmental Behavior in an Aging World: Evidence from 31 Countries

2021 ◽  
Vol 18 (4) ◽  
pp. 1748
Author(s):  
Yan Wang ◽  
Feng Hao ◽  
Yunxia Liu
Keyword(s):  
Linear Models ◽  
Population Change ◽  
Population Aging ◽  
Environmental Behavior ◽  
National Level ◽  
The Elderly ◽  
Human Beings ◽  
Individual Level ◽  
Contemporary World ◽  
The Individual

Population change and environmental degradation have become two of the most pressing issues for sustainable development in the contemporary world, while the effect of population aging on pro-environmental behavior remains controversial. In this paper, we examine the effects of individual and population aging on pro-environmental behavior through multilevel analyses of cross-national data from 31 countries. Hierarchical linear models with random intercepts are employed to analyze the data. The findings reveal a positive relationship between aging and pro-environmental behavior. At the individual level, older people are more likely to participate in environmental behavior (b = 0.052, p < 0.001), and at the national level, living in a country with a greater share of older persons encourages individuals to behave sustainably (b = 0.023, p < 0.01). We also found that the elderly are more environmentally active in an aging society. The findings imply that the longevity of human beings may offer opportunities for the improvement of the natural environment.

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