Sukwon Kim

Tennis forehand is a tricky activity that employs practically all parts of a body. The legs and the trunk are important as well as the arm and the wrist, as they determine the speed with which the racket attains movement, and influence the mastery that the player is going to have over the ball when it strikes and is entered. Surface electromyography (sEMG) is a non-invasive method of recording muscle switching on and the intensity of the work. Kinematic and wearable-sensor data provide an addition of a picture of the joint motion in the body segments. This article gathers previously known biomechanical evidence of the forehand as well as extending it into a technology-centered paradigm of AI-assisted coaching. Recent studies indicate that not all players who activate their muscles more are necessarily stronger. It seems to be their benefit of a superior timing and improved alignment of the segments, as well as the accelerated movement of the swing. The suggested paradigm begins with the combination of sEMG and inertial or kinematic measurements. Signals are initially cleansed. Attributes are subsequently obtained and fed through machine-learning algorithms that identify movement patterns. The last steps are error identification and personalized coaching feedback. What this might imply to practical teaching (core-centered movement instruction and control of excessive co-contraction) is also given due consideration in the article. The concepts of the wrist and forearm control and ground reaction force awareness are also discussed. The framework is theoretical and not clinically or performance validated. Annotated multimodal data sets should be built in future work and contrasting machine-learning models should be compared. It will also need to evaluate their generalization between players and skill levels, and whether AI-generated feedback does indeed positively affect the measurable tennis results.

This study examined the effects of ballistic training (BT), blood flow restriction training (BFRT), and traditional resistance training (TRT) on the smash performance of adolescent badminton players. Thirty athletes were randomly assigned to BT, BFRT, or TRT groups and completed 12 training sessions over four weeks. Heart rate (HR) was continuously monitored to adjust intensity and rest intervals. After the intervention, data were analyzed using two-way mixed-design ANOVA. Smash speed significantly improved across all groups (F(1,27) = 22.34, p < 0.05, η²ₚ = 0.66), with a significant time by group interaction (F(2,27) = 6.71, p = 0.004, η²ₚ = 0.33). In the BFRT group, smash speed increased from 166.8 ± 9.7 to 202.0 ± 11.3 km·h⁻¹. Smash accuracy improved slightly without significant group differences (F(2,27) = 4.23, p = 0.026, η²ₚ = 0.24). Exercise heart rate and recovery showed significant time and time by group effects (Ex HR: F(2,27) = 7.08, p = 0.003; HRR: F(2,27) = 5.42, p = 0.010), while resting HR remained stable. In conclusion, both BT and BFRT increase smash speed in adolescent badminton players, suggesting that combining these methods can improve performance and training efficiency.

The use of artificial intelligence (AI) in sport is often presented as a technical advance, yet its coaching value depends on a simpler question: can the result be explained in movement terms? This review revisits current AI applications in sport from a biomechanical perspective. It considers computer vision, markerless motion capture, wearable monitoring, injury-risk modeling, explainable AI, coordination theory, muscle synergy research, and sport-specific movement studies. The central argument is that a useful sport AI system should not stop at prediction. It should show how the athlete produced the performance through segment coordination, joint loading, postural control, and energy transfer. To organize this argument, the paper proposes a four-layer model: capture, interpretation, decision, and intervention. The model places AI between measurement and coaching, while biomechanical theory remains the foundation for deciding which variables matter. The review also suggests that personal sport records become meaningful only when linked to validated movement variables and coach-readable explanations. In this view, the next step for AI in sport is not merely larger datasets, but more interpretable, task-specific, and coordination-aware feedback.

Background: Effective neuromuscular coordination during the basketball jump shot is essential for optimizing performance and reducing injury risk. Inconsistent temporal sequencing of muscle activation…

Objectives: To examine the effects of on-court movement distances on pelvis-trunk coordination during forehand strokes. Methods: Eighteen male college tennis athletes participated in this study. They performed forehand stroke task at three different preparatory court movement distances (minimum, medium and maximum movement distances). A one-way repeated-measures ANOVA with Statistical Parametric Mapping was used to determine differences in pelvis-trunk coordination in the transverse plane across the three movement distances, and Pearson’s correlation analysis was used to determine the relationships between each of the four pelvis-trunk coordination features on the dominant and non-dominant side and racket speed. Results: Significant differences were observed for different movement distances in the non-dominant pelvis-trunk continuous relative phase (CRP) during 23–41% of the acceleration phase ( p = 0.016, F _2,34 = 5.901) and in the dominant pelvis-trunk CRP during 76–100% of the acceleration phase ( p = 0.016, F _2,34 = 5.946). For the minimum distance, significant correlations with racket speed were found in the mean CRP ( r = − 0.889, p = 0.001) and peak CRP ( r = − 0.488, p = 0.04) for the non-dominant side, and the mean CRP ( r = − 0.478, p = 0.045) for the dominant side. Regarding medium distances, significant correlations with racket speed were observed for the non-dominant side in the mean CRP ( r = − 0.493, p = 0.037), peak CRP ( r = − 0.628, p = 0.005), and maximum positive CRP slope ( r = 0.477, p = 0.046). For the dominant side, significant correlations with racket speed were noted for peak CRP ( r = 0.551, p = 0.018) and maximum positive CRP slope ( r = 0.514, p = 0.029). At the maximum distance, significant correlations with racket speed were identified for the dominant side in the maximum positive CRP slope ( r = 0.580, p = 0.012) and maximum negative CRP slope ( r = 0.566, p = 0.014); however, there was no significant difference in racket speed at impact when approaching from different distances. Conclusion: These findings underscore the role of pelvis-trunk coordination in enhancing racket speed, particularly under varying task constraints. Coaches and players should focus on developing adaptable coordination strategies for optimizing performance across different movement distances.

This study investigated the activation of major lower limb muscles during isometric squats (ISs) at different knee angles and their effects as a conditioning activity (CA) on squat jump (SJ) performance at varying initial knee angles. Twelve collegiate sprinters were randomly assigned to three conditions: control (CC), 90°IS condition (90°ISC), and 120°IS condition (120°ISC), using a balanced crossover design. EMG data from five lower limb muscles were collected during ISs, alongside the peak center of mass velocity (V peak), peak force (F peak), peak power (P peak), average power (P avg), and the force-velocity ratio at peak power (SFV) during subsequent SJs. Results showed that the 90°IS condition elicited higher rectus femoris (p = 0.035, d = 0.729) activation, while the 120°IS condition led to greater soleus (p = 0.030, d = 0.759), and tibialis anterior (p = 0.022, d = 0.812) activation. Both the 90°ISC and the 120°ISC significantly increased peak velocity (90°ISC: p < 0.001, d = 1.379; 120°ISC: p < 0.001, d = 0.979) compared to the CC, but showed no significant improvements in F peak or P peak. Notably, the SFV during the 90° and 120°SJ was significantly lower in the 90°ISC and the 120°ISC compared to the CC (90°ISC: p < 0.001, d = −2.658; 120°ISC: p < 0.001, d = −1.785). These findings suggest that the IS at different knee angles selectively activates lower limb muscles and enhances velocity-related performance metrics. Specific IS angles can alter the force-velocity relationship, increasing the velocity component at peak power during subsequent movements. Coaches and athletes in speed-dependent sports are encouraged to select IS angles tailored to their specific training goals and performance needs.

Background: Prior studies have examined various determinants of college students’ physical activity behaviors; however, limited attention has been given to the role of a supportive environment within physical education courses. According to Pekrun’s control-value theory of achievement emotions, individuals’ appraisals of control and value in specific learning contexts evoke corresponding achievement emotions, which subsequently influence their learning behaviors and outcomes. Objective: This study aimed to examine how a supportive teaching environment in physical education classes influences college students’ physical activity behaviors through control-value appraisals and achievement emotions, contributing to a broader application of the control-value theory of achievement emotions. Method: A total of 1,562 college students from five universities in China completed questionnaires, including the Physical Education Demand Support Questionnaire, Academic Control Scale, Subjective Task Value Scale of Sports, Achievement Emotions Questionnaire, and Physical Activity Rank Scale-3. Structural equation modeling (SEM) was used to validate the relationships. Results: Control-value appraisals and two achievement emotions—enjoyment and boredom—played partial mediating roles in the relationship between a supportive teaching environment and college students’ physical activity behaviors. The chain indirect effects through control appraisal and enjoyment, and through control appraisal and boredom, were 0.022 and 0.008, respectively. Similarly, the indirect effects through value appraisal and enjoyment, and through value appraisal and boredom, were 0.034 and 0.010, respectively. Conclusion: Teacher autonomy support indirectly influenced college students’ physical activity behaviors through control-value appraisals and achievement emotions, particularly enjoyment and boredom. Enjoyment emerged as an important achievement emotion that positively shaped exercise motivation and behavior, highlighting the critical role of emotional experiences in linking supportive teaching environments with students’ physical activity. These findings extend the control-value theory of achievement emotions to the context of physical education and provide empirical evidence for promoting teacher autonomy support to foster students’ exercise engagement.

The primary objective of the study was to evaluate the risk of lower-extremity injury during landing, after catching a basketball in the air for a rebound or a steal. A total of 32 (15 females and 17 males) recreational basketball players were asked to perform 4 different arm-reaching movements during the flight phase: no reaching, left (LAR), right (RAR), and bilateral (BAR) arm reaching. Knee and hip flexion and abduction angles at initial ground contact, peak ground reaction force within 100 milliseconds after contact, and peak moments for the hip and knee joints were analyzed. LAR, RAR, and BAR showed significantly smaller peak hip and knee flexion angles, while LAR demonstrated a significant increase in the knee’s peak abduction angle. Furthermore, the peak abduction moments of LAR were significantly increased in both males and females. Aerial basketball catching movements resulted in stiffer leg joints and larger knee abduction during landing, which was a potential factor in increasing the risk of lower-limb injury upon landing. It is recommended to include a softer landing technique and implement a training program for enhancing knee joint stabilization in the frontal plane.

This study aimed to explore the impact of playing experience on vertical release velocity (VV) and horizontal release velocity (HV) during mid- and long-distance jump shots, and the relationships among VV, HV, and the contraction intensity levels of primary muscles. The study utilized a cross-sectional experimental design, with 30 participants completing three effective jump shots at distances of 4.8 m and 6.75 m. Data were collected using a 3D motion capture system and an electromyography (EMG) recording system, with velocity-time data extracted to determine VV and HV, and EMG data analyzed using wavelet analysis. A two-factor mixed ANOVA was used to compare differences in VV and HV under different conditions, and Spearman correlation analysis was employed to explore relationships among VV, HV, and the energy values of major muscle frequency bands. The results indicated that playing experience significantly improved VV (p < 0.001). At mid-range distances, experienced athletes' VV positively correlated with the low frequency band (r = 0.479, p = 0.048) and near frequency band (r = 0.539, p = 0.038) energy values of the triceps brachii (TB), as well as the high frequency band (r = 0.679, p = 0.005) and mid-high frequency band (r = 0.571, p = 0.026) energy values of the anterior deltoid (AD). At long-range distances, VV negatively correlated with the low frequency band energy values of the flexor carpi radialis (r = −0.5, p = 0.045), the high frequency band of TB (r = −0.564, p = 0.028), and the near frequency band of AD (r = −0.496, p = 0.046). These findings suggest that playing experience significantly alters VV during jump shots, and this alteration may be associated with the overall activity of the TB, the explosive activity of the AD, and the baseline activity of the rectus femoris.