Search Results (6)

In tennis, where performance is governed by complex kinetic chain interactions, objective skill classification is vital for coaching and talent identification. This study presents a hierarchical deep learning framework leveraging synchronized bilateral Inertial Measurement Unit (IMU) data from 39 participants (11 elite, 28 amateur). The proposed system successfully distinguishes expertise levels across a total of 4594 strokes, including augmented samples. A hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) architecture was developed to autonomously extract spatiotemporal features from the raw kinematic signals of forehand, backhand, service, and volley strokes. The proposed model achieved an accuracy of 95.54%, significantly outperforming both traditional machine learning and state-of-the-art deep learning benchmarks. Qualitative t-distributed Stochastic Neighbor Embedding (t-SNE) analyses revealed that elite athletes form highly homogeneous clusters in the feature space. Furthermore, quantitative Asymmetry Index assessments confirmed that professionals exhibit superior bilateral coordination stability. These findings demonstrate that the proposed end-to-end system offers a robust, field-applicable solution for identifying technical excellence. It provides coaches with reliable digital biomarkers, thereby overcoming the limitations of subjective visual observation. Full article

The ability to inhibit conflicting information is pivotal in the dynamic and high-speed context of fast-ball sports. However, the behavioral and electrophysiological characteristics underlying the cognitive inhibition processes associated with table tennis expertise remain unexplored. This study aims to bridge these research gaps by utilizing the color-word Stroop task and the spatial Stroop task alongside event-related potential (ERP) measurements to investigate domain-general and domain-specific cognitive inhibition among table tennis athletes. The study involved a total of 40 participants, including 20 table tennis athletes (11 males and 9 females; mean age 20.75 years) and 20 nonathletes (9 males and 11 females; mean age 19.80 years). The group differences in the Stroop effect on behavioral outcomes and ERP amplitudes were compared within each task, respectively. In the color-word Stroop tasks, athletes exhibited smaller incongruent-related negative potential amplitudes (N_inc; 300–400 ms; p = 0.036) and a diminished Stroop effect on late sustained potential amplitudes (LSP; 500–650 ms; p = 0.028) than nonathletes, although no significant differences were observed in behavioral outcomes (p > 0.05). Conversely, in the spatial Stroop tasks, athletes not only responded more swiftly but also exhibited reduced Stroop effects on both LSP amplitudes (350–500 ms; p = 0.004) and reaction times (p = 0.002) relative to nonathletes. These findings suggest that table tennis athletes excel in cognitive inhibition in the context of both domain-general and domain-specific tasks, particularly exhibiting enhanced performance in tasks that are closely aligned with the demands of their sport. Our results support the neural efficiency hypothesis and improve our understanding of the interactions between cognitive functions and table tennis expertise. Full article

Visual motion information plays an important role in the control of movements in sports. Skilled ball players are thought to acquire accurate visual information by using an effective visual search strategy with eye and head movements. However, differences in catching ability and gaze movements due to sports experience and expertise have not been clarified. Therefore, the purpose of this study was to determine the characteristics of gaze strategies based on eye and head movements during a ball-catching task in athlete and novice groups. Participants were softball and tennis players and college students who were not experienced in ball sports (novice). They performed a one-handed catching task using a tennis ball-shooting machine, which was placed at 9 m in front of the participants, and two conditions were set depending on the height of the ball trajectory (high and low conditions). Their head and eye velocities were detected using a gyroscope and electrooculography (EOG) during the task. Our results showed that the upward head velocity and the downward eye velocity were lower in the softball group than in the tennis and novice groups. When the head was pitched upward, the downward eye velocity was induced from the vestibulo-ocular reflex (VOR) during ball catching. Therefore, it is suggested that skilled ball players have relatively stable head and eye movements, which may lead to an effective gaze strategy. An advantage of the stationary gaze in the softball group could be to acquire visual information about the surroundings other than the ball. Full article

Tennis experts need to extract effective visual information from a sphere in high-speed motion, in which motion-in-depth perception plays an important role. The purpose of the current study was to investigate the impact of sphere speed and tennis expertise on motion-in-depth perception by using the expert–novice task paradigm along with event-related potential (ERP) technology. The study also explored differences in behavior and electroencephalogram (EEG) characteristics between tennis experts and novices. Results show that faster sphere movement led to shorter response times and a lower accuracy rate. The P1 component in the occipital–temporal region showed that the expert group activated earlier and were stronger when the sphere was far away. The latent period of P2 in the occipital region was significantly shorter in the expert group in comparison to the novice group. Faster speed led to the induction of increased P300 volatility and a significant increase in latency. The findings of the current study show that the speed of the sphere movement affects the invocation and allocation of cognitive resources in the process of motion-in-depth perception, irrespective of whether the athletes were experts or novices. There is a special effect in the process of motion-in-depth perception for experts, mainly because attention resources are invested earlier in experts rather than novices. Full article

Tennis champion Maria Sharapova has a habit of grunting when she plays on the court. Assume that she also has a habit of hitting the ball in a certain way in a certain situation. The habit of on-court grunting might be bad, but can the habit of hitting the ball in a certain way in a certain situation be classified as intelligent? The fundamental questions here are as follows: What is habit? What is the relation between habit and skill? Is there such a thing as intelligent habit? In this paper I expound the nature of habit by developing and defending a Rylean conception of habit, according to which an acquired disposition is a habit if and only if the manifestation of the disposition is repeated, automatic, and uniform. One implication of this conception is that there is no such thing as intelligent habit. A practical application in athletic expertise is that sport coaches can help athletes go beyond repeated, automatic, and uniform dispositions in sport. Full article

Motion capture (Mocap) data are widely used as time series to study human movement. Indeed, animation movies, video games, and biomechanical systems for rehabilitation are significant applications related to Mocap data. However, classifying multi-channel time series from Mocap requires coding the intrinsic dependencies (even nonlinear relationships) between human body joints. Furthermore, the same human action may have variations because the individual alters their movement and therefore the inter/intraclass variability. Here, we introduce an enhanced Hilbert embedding-based approach from a cross-covariance operator, termed EHECCO, to map the input Mocap time series to a tensor space built from both 3D skeletal joints and a principal component analysis-based projection. Obtained results demonstrate how EHECCO represents and discriminates joint probability distributions as kernel-based evaluation of input time series within a tensor reproducing kernel Hilbert space (RKHS). Our approach achieves competitive classification results for style/subject and action recognition tasks on well-known publicly available databases. Moreover, EHECCO favors the interpretation of relevant anthropometric variables correlated with players’ expertise and acted movement on a Tennis-Mocap database (also publicly available with this work). Thereby, our EHECCO-based framework provides a unified representation (through the tensor RKHS) of the Mocap time series to compute linear correlations between a coded metric from joint distributions and player properties, i.e., age, body measurements, and sport movement (action class). Full article