Utilization of cues in action anticipation in table tennis players.

By manipulating the congruency between body kinematics and subsequent ball trajectory, this study investigated the anticipation capabilities of regional-level, college-level, and novice table tennis players using a full video simulation occluder paradigm. Participants watched footage containing congruent, incongruent, or no ball trajectory information, to predict the landing point of the ball. They were required to choose between two potential locations to make their prediction. Percent accuracy and relevant indexes (d-prime, criterion, effect size) were calculated for each condition. Results indicated that experienced table tennis players (both regional and college players) were superior to novices in the ability to anticipate ball trajectory using kinematic information, but no difference was found between regional-level and college-level players. The findings of this study further demonstrate the superior anticipation ability of experienced table tennis players. Furthermore, the present result suggests that there may be a certain "baseline" level of motor experience in racquet sports for effective action anticipation, while the addition of further motor experience does not appear to assist direction anticipation.

A review on the basketball jump shot.

The ability to shoot an effective jump shot in the sport of basketball is critical to a player's success. In an attempt to better understand the aspects related to expert performance, researchers have investigated successful free throws and jump shots of various basketball players and identified movement variables that contribute to their success. The purpose of this study was to complete a systematic review of the scientific literature on the basketball free throw and jump shot for the purpose of revealing the critical components of shooting that coaches, teachers, and players should focus on when teaching, learning, practising, and performing a jump shot. The results of this review are presented in three sections: (a) variables that affect ball trajectory, (b) phases of the jump shot, and

Mechanisms of speed-accuracy trade-off in tennis topspin forehand of college players.

This study aimed to elucidate the mechanisms underlying the speed-accuracy relationship in a tennis topspin single-handed forehand groundstroke. Groundstrokes at three different speeds by nine college players were captured, with the variability of the ball landing position evaluated as indices of accuracy. Variabilities of ball launch variables (speed, launch angle, spin, etc.) and conversion ratios from these variabilities to the variability of the ball landing position were quantified. These variabilities and their conversion ratios could be influenced by different efforts exerted to generate ball speeds and different ball trajectories required to hit a target at each speed, respectively. The speed-accuracy trade-off was observed only in the hitting direction. While the variability of the spin axis increased, it had minimal influence on the ball landing position. Conversely, the conversion ratio in the hitting direction of the velocity elevation angle significantly increased, while its variability remained unchanged. Consequently, the geometrical requirements of ball trajectories can be responsible for the speed-accuracy trade-off. Therefore, even skilled players capable of maintaining consistent ball launch variables regardless of the shot speed should deliberately choose a moderately slower ball speed to avoid an inevitable increase in the variability of the ball landing position due to geometrical requirements.

Can golfers choose low-risk routes in steep putting based on visual feedback of ball trajectory?

This study aims to clarify why the aiming method in golf putting in risky situations differs based on skill level. This study set up a difficult challenge (steep slopes and fast ball rolling greens), which required even professional golfers to change their aim. A total of 12 tour professionals and 12 intermediate amateurs were asked to perform a steep-slope task with no visual feedback of outcomes (no FB) followed by a task with visual feedback (with FB). The aim of the task was for the ball to enter the hole in one shot. Additionally, the participants were told that if the ball did not enter the hole, it was to at least stop as close to it as possible. The participant's aim (as an angle) and the kinematics of the putter head and ball were measured. The results indicated that professionals' highest ball trajectory points were significantly higher than that of amateurs, especially with FB. Additionally, professionals had higher ball-launch angles (the direction of the ball when the line connecting the ball and the center of the hole is 0 degrees) and lower peak putter head velocities than amateurs. Furthermore, the aim angle, indicating the golfer's decision-making, was higher for professionals under both conditions. However, even with FB, the amateurs' aim angles were lower and the difference between trials was smaller than that of professionals. Therefore, this study confirmed that the professionals made more drastic changes to their aim to find low-risk routes than the amateurs and that the amateurs' ability to adjust their aim was lower than that of professionals. The results suggest that the reason for the amateurs' inability to find low-risk routes lies in their decision-making. The professionals found better routes; however, there were individual differences in their routes.

Event-Based Trajectory Prediction Using Spiking Neural Networks.

In recent years, event-based sensors have been combined with spiking neural networks (SNNs) to create a new generation of bio-inspired artificial vision systems. These systems can process spatio-temporal data in real time, and are highly energy efficient. In this study, we used a new hybrid event-based camera in conjunction with a multi-layer spiking neural network trained with a spike-timing-dependent plasticity learning rule. We showed that neurons learn from repeated and correlated spatio-temporal patterns in an unsupervised way and become selective to motion features, such as direction and speed. This motion selectivity can then be used to predict ball trajectory by adding a simple read-out layer composed of polynomial regressions, and trained in a supervised manner. Hence, we show that a SNN receiving inputs from an event-based sensor can extract relevant spatio-temporal patterns to process and predict ball trajectories.