A machine learning approach for automatic detection and classification of changes of direction from player tracking data in professional tennis

Optimizing young tennis players' development: Exploring the impact of emerging technologies on training effectiveness and technical skills acquisition

The research analyzed the effect of weekly training plans, physical training frequency, AI-powered coaching systems, virtual reality (VR) training environments, wearable sensors on developing technical tennis skills, with and personalized learning as a mediator. It adopted a quantitative survey method, using primary data from 374 young tennis players. The model fitness was evaluated using confirmatory factor analysis (CFA), while the hypotheses were evaluated using structural equation modeling (SEM). The model fitness was confirmed through CFA, demonstrating high fit indices: CFI = 0.924, TLI = 0.913, IFI = 0.924, RMSEA = 0.057, and SRMR = 0.041, indicating a robust model fit. Hypotheses testing revealed that physical training frequency (β = 0.198, p = 0.000), AI-powered coaching systems (β = 0.349, p = 0.000), virtual reality training environments (β = 0.476, p = 0.000), and wearable sensors (β = 0.171, p = 0.000) significantly influenced technical skills acquisition. In contrast, the weekly training plan (β = 0.024, p = 0.834) and personalized learning (β = -0.045, p = 0.81) did not have a significant effect. Mediation analysis revealed that personalized learning was not a significant mediator between training methods/technologies and acquiring technical abilities. The results revealed that physical training frequency, AI-powered coaching systems, virtual reality training environments, and wearable sensors significantly influenced technical skills acquisition. However, personalized learning did not have a significant mediation effect. The study recommended that young tennis players' organizations and stakeholders consider investing in emerging technologies and training methods. Effective training should be given to coaches on effectively integrating emerging technologies into coaching regimens and practices.

Quantifying Change of Direction Movement Demands in Professional Tennis Matchplay: An Analysis From the Australian Open Grand Slam

ABSTRACT

Giles, B, Peeling, P, and Reid, M. Quantifying change of direction movement demands in professional tennis matchplay: An analysis from the Australian Open Grand Slam. J Strength Cond Res 38(3): 517-525, 2024-Change of direction (COD) contributes significantly to the movement repertoire of professional tennis players, yet the time-motion and degree demands of these changes are poorly understood. This study examines the velocity, acceleration, and angular displacement profiles of COD movements in modern professional tennis. One hundred eighty-two singles matches of Hawk-Eye player tracking data collected from the Australian Open Grand Slam were used for analysis. A novel COD classification algorithm was used to identify >120,000 medium and high-intensity CODs for analysis. Descriptive characteristics of the COD performance were calculated using player coordinate and time variables. Sex comparisons were analyzed using 2 mixed-effects models assessed for differences via likelihood ratios. Players performed 1.6 CODs per point. Both sexes executed, on average, 1.3-1.4 shots and covered 4.8 m per COD, with men performing changes every 2.7 seconds and women every 3.1 seconds. Medium-intensity COD comprised 88-94% of all identified changes. Approximately 2 in 3 CODs involved a degree of change >105°, whereas cutting maneuvers (<45°) were most commonly high-intensity COD. This study is the first to quantify the COD characteristics of professional tennis matchplay. Both sexes performed the same average number of CODs per point, however, men executed high-intensity changes twice as frequently as women, at an average of 1 every 5 points. These novel findings will help to improve the specificity of training interventions in elite tennis conditioning.

Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network

This study explores the integration of artificial intelligence (AI) teaching assistants in sports tennis instruction to enhance the intelligent teaching system. Firstly, the applicability of AI technology to tennis teaching in schools is investigated. The intelligent teaching system comprises an expert system, an image acquisition system, and an intelligent language system. Secondly, employing compressed sensing theory, a framework for learning the large-scale fuzzy cognitive map (FCM) from time series data, termed compressed sensing-FCM (CS-FCM), is devised to address challenges associated with automatic learning methods in the designed AI teaching assistant system. Finally, a high-order FCM-based time series prediction framework is proposed. According to experimental simulations, CS-FCM demonstrates robust convergence and stability, achieving a stable point with a reconstruction error below 0.001 after 15 iterations for FCM with various data lengths and a density of 20%. The proposed intelligent system based on high-order complex networks significantly improves upon the limitations of the current FCM model. The advantages of its teaching assistant system can be effectively leveraged for tennis instruction in sports.

From The Ground Up: Expert Perceptions of Lower Limb Activity Monitoring in Tennis

Understanding on-court movement in tennis allows for enhanced preparation strategies to improve player readiness and performance. Here, we explore expert physical preparation coaches' perceptions of elite training strategies for preparation and performance in tennis, with special reference to lower limb activity. Thirteen world renowned tennis strength and conditioning coaches were interviewed in a semi-structured method that explored four key topic areas of physical preparation for tennis: i) the physical demands; ii) load monitoring practice; iii) the direction of ground reaction forces application during match-play; and iv) the application of strength and conditioning for tennis. Three higher-order themes emerged from these discussions: i) off-court training for tennis should be specific to the demands of the sport, ii) the mechanical understanding of tennis lags our physiological approach, and iii) our understanding of the lower limb's contribution to tennis performance is limited. These findings provide valuable insights into the importance of improving our knowledge relevant to the mechanical demands of tennis movement, whilst highlighting important practical considerations from leading tennis conditioning experts.

Perspective on "in the wild" movement analysis using machine learning

Recent advances in wearable sensing and machine learning have created ample opportunities for "in the wild" movement analysis in sports, since the combination of both enables real-time feedback to be provided to athletes and coaches, as well as long-term monitoring of movements. The potential for real-time feedback is useful for performance enhancement or technique analysis, and can be achieved by training efficient models and implementing them on dedicated hardware. Long-term monitoring of movement can be used for injury prevention, among others. Such applications are often enabled by training a machine learned model from large datasets that have been collected using wearable sensors. Therefore, in this perspective paper, we provide an overview of approaches for studies that aim to analyze sports movement "in the wild" using wearable sensors and machine learning. First, we discuss how a measurement protocol can be set up by answering six questions. Then, we discuss the benefits and pitfalls and provide recommendations for effective training of machine learning models from movement data, focusing on data pre-processing, feature calculation, and model selection and tuning. Finally, we highlight two application domains where "in the wild" data recording was combined with machine learning for injury prevention and technique analysis, respectively.

Differentiating movement styles in professional tennis: A machine learning and hierarchical clustering approach

PURPOSE

Recent explorations of tennis-specific movements have developed contemporary methods for identifying and classifying changes of direction (COD) during match-play. The aim of this research was to employ these new analysis techniques to objectively explore individual nuance and style factors in the execution of COD movements in professional tennis.

METHODS

Player tracking data from 62 male and 77 female players at the Australian Open Grand Slam were analysed for COD movements using a model algorithm, with a sample of 150,000 direction changes identified. Hierarchical clustering methods were employed on the time-motion and degree characteristics of these direction changes to identify groups of different COD performers.

RESULTS

Five unique clusters, labelled "Cutters", "Gear Changers", "Lateral Changers", "Balanced Changers" and "Passive Changers" were identified in accordance with their varying speed, acceleration, degree and directionality of change features.

CONCLUSIONS

Player COD clustering challenge previously held assumptions regarding on-court movement style, highlighting the complexity and variation in the sport's locomotion demands. In practice, the speed, acceleration, directionality and degree of change characteristics of each COD style can facilitate athlete profiling and the specificity of training interventions.HighlightsWe used machine learning techniques and cluster analysis methodology to explore the time motion characteristics of direction change skill in professional tennis.We present five unique types of change of direction style in professional tennis players. These include "Cutters", "Gear Changers", "Lateral Changers", "Balanced Changers" & "Passive Changers". These style classifications were established in accordance with their varying speed, acceleration, degree and directionality of change features.We show that the application of machine learning techniques to player tracking data can facilitate a more intricate understanding the sport's physical demands, which can be used to inform training programme design.

SPORT KINESIOLOGY BASED ON THE CONCEPT OF HEALTH AND FITNESS

ABSTRACT Introduction: Exercise is the most effective way to improve physical fitness. One can achieve the effect of wellness and fitness through scientific exercise. Running is a relatively common method of physical exercise. It plays a significant role in improving physical fitness. Objective: This study aimed to investigate the characteristics of lower extremity movements during running. The results of this study may provide better exercise planning for runners. Methods: This paper selects several runners as the research subject. The subjects started running after attaching a motion detector sensor patch to their body. Then, this paper collected kinematic data. The kinematic data includes the joint angles and range of motion (ROM) of the hip, knee, and ankle joints. Results: There was no significant difference in the distribution of peak tibial acceleration, plantar pressure, and maximum pressure of athletes under different track materials (P>0.05). There was a significant age difference between the hip and knee joints of the athletes in the overhead stage (P<0.05). Conclusion: There may not be a necessary connection between ground and lower limb impact in running athletes. Through its adjustment, the human body can dampen the load effect of the foot contact surface. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

MEASUREMENT INDEX SYSTEM OF SPECIFIC PHYSICAL TRAINING FOR TENNIS ATHLETES

ABSTRACT Introduction: Tennis is a competitive sport endowed with subtle movements, sophisticated and changeable techniques and tactics, and intense confrontation. It has high demands on the athlete's physical and psychological qualities. To win high-level professional tennis events, in addition to basic skills, comprehensive technical and tactical abilities, stable psychological quality, and flexible and innovative thinking, tennis players must also have exceptional physical fitness. Objective: Study the index system for evaluating the sport-specific skills of tennis athletes. Methods: 30 youth tennis training athletes were selected. Research methods such as literature, expert interview, questionnaire, and mathematical statistics were used to construct the fitness evaluation index of Chinese professional tennis players. Results: Professional tennis players’ specific fitness assessment indexes include one first-level index, 14 second-level indexes, and 23 three-item indexes. Conclusion: Young athletes must strengthen their agile attack speed, explosive strength, core strength, and coordination. The individual indicators are weighted according to individual standards of physical training level. Thus, the five first-level indicators and specific fitness standards are established in this paper. The results of this research have guiding significance for the formulation and implementation of further tennis education and training plans. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

Association between the level of partial foot amputation and gait: a scoping review with implications for the minimum impairment criteria for wheelchair tennis

OBJECTIVE

This scoping review examines how different levels and types of partial foot amputation affect gait and explores how these findings may affect the minimal impairment criteria for wheelchair tennis.

METHODS

Four databases (PubMed, Embase, CINAHL and SPORTDiscus) were systematically searched in February 2021 for terms related to partial foot amputation and ambulation. The search was updated in February 2022. All study designs investigating gait-related outcomes in individuals with partial foot amputation were included and independently screened by two reviewers based on Arksey and O'Malley's methodological framework and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews.

RESULTS

Twenty-nine publications with data from 252 participants with partial foot amputation in 25 studies were analysed. Toe amputations were associated with minor gait abnormalities, and great toe amputations caused loss of push-off in a forward and lateral direction. Metatarsophalangeal amputations were associated with loss of stability and decreased gait speed. Ray amputations were associated with decreased gait speed and reduced lower extremity range of motion. Transmetatarsal amputations and more proximal amputations were associated with abnormal gait, substantial loss of power generation across the ankle and impaired mobility.

CONCLUSIONS

Partial foot amputation was associated with various gait changes, depending on the type of amputation. Different levels and types of foot amputation are likely to affect tennis performance. We recommend including first ray, transmetatarsal, Chopart and Lisfranc amputations in the minimum impairment criteria, excluding toe amputations (digits two to five), and we are unsure whether to include or exclude great toe, ray (two to five) and metatarsophalangeal amputations.

TRIAL REGISTRATION

The protocol of this scoping review was previously registered at the Open Science Framework Registry (https://osf.io/8gh9y) and published.

Research on Optimal Control Method of Tennis Racket String Diameter Based on Kalman Filter Algorithm

In order to explore the optimal control method of tennis racket string diameter, this paper applies the Kalman filter algorithm to the collection and processing of tennis sports parameters. When the Kalman filter uses the optimal gain (the Kalman filter enters the steady state), the corresponding cost function is established based on the noncorrelated nature of its residual sequence, which is used as the judgment condition for the sampling strategy switch to improve the stability of the system. In addition, this paper improves the real-time performance and calculation accuracy of data transformation through adaptive sampling strategy and adaptive scale factor, improves the stability and estimation accuracy of the system as a whole, and builds an intelligent monitoring system. Finally, this paper systematically studied the optimization control method of tennis racket string diameter and verified that the Kalman filter algorithm can play a certain role in the optimization control of tennis racket string diameter.

Return Strategy and Machine Learning Optimization of Tennis Sports Robot for Human Motion Recognition

At present, there are many kinds of intelligent training equipment in tennis sports, but they all need human control. If a single tennis player uses the robot to return the ball, it will save some human resources. This study aims to improve the recognition rate of tennis sports robots in the return action and the return strategy. The human-oriented motion recognition of the tennis sports robot is taken as the starting point to recognize and analyze the return action of the tennis sports robot. The OpenPose traversal dataset is used to recognize and extract human motion features of tennis sports robots under different classifications. According to the return characteristics of the tennis sports robot, the method of tennis return strategy based on the support vector machine (SVM) is established, and the SVM algorithm in machine learning is optimized. Finally, the return strategy of tennis sports robots under eight return actions is analyzed and studied. The results reveal that the tennis sports robot based on the SVM-Optimization (SVM-O) algorithm has the highest return recognition rate, and the average return recognition rate is 88.61%. The error rates of the backswing, forward swing, and volatilization are high in the return strategy of tennis sports robots. The preparation action, backswing, and volatilization can achieve more objective results in the analysis of the return strategy, which is more than 90%. With the increase of iteration times, the effect of the model simulation experiment based on SVM-O is the best. It suggests that the algorithm proposed has a reliable accuracy of the return strategy of tennis sports robots, which meets the research requirements. Human motion recognition is integrated with the return motion of tennis sports robots. The application of the SVM-O algorithm to the return action recognition of tennis sports robots has good practicability in the return action recognition of tennis sports robot and solves the problem that the optimization algorithm cannot be applied to the real-time requirements. It has important research significance for the application of an optimized SVM algorithm in sports action recognition.

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports such as soccer, basketball, cricket, and badminton, studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications: high-level analysis such as detection and classification of players, tracking players or balls in sports and predicting the trajectories of players or balls, recognizing the team’s strategies, and classifying various events in sports. The paper further discusses published works in a variety of application-specific tasks related to sports and the present researcher’s views regarding them. Since there is a wide research scope in sports for deploying computer vision techniques in various sports, some of the publicly available datasets related to a particular sport have been discussed. This paper reviews detailed discussion on some of the artificial intelligence (AI) applications, GPU-based work-stations and embedded platforms in sports vision. Finally, this review identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports.

Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis

In view of the intelligent demand of tennis line examination, this paper performs a systematic analysis on the intelligent recognition of tennis line examination. Then, a tennis line recognition method based on machine vision is proposed. In this paper, the color region of the image recognition region is divided based on the region growth, and the rough estimation of the court boundary is realized. In order to achieve the effect of camera calibration, a fast camera calibration method which can be used for a variety of court types is proposed. On the basis of camera calibration, a tennis line examination and segmentation system based on machine vision analysis is constructed, and the experimental results are verified by design experiments. The results show that the machine vision analysis-based intelligent segmentation system of tennis line examination has high recognition accuracy and can meet the actual needs of tennis line examination.

Prognostic Validity of Statistical Prediction Methods Used for Talent Identification in Youth Tennis Players Based on Motor Abilities

(1) Background: The search for talented young athletes is an important element of top-class sport. While performance profiles and suitable test tasks for talent identification have already been extensively investigated, there are few studies on statistical prediction methods for talent identification. Therefore, this long-term study examined the prognostic validity of four talent prediction methods. (2) Methods: Tennis players (N = 174; n♀ = 62 and n♂ = 112) at the age of eight years (U9) were examined using five physical fitness tests and four motor competence tests. Based on the test results, four predictions regarding the individual future performance were made for each participant using a linear recommendation score, a logistic regression, a discriminant analysis, and a neural network. These forecasts were then compared with the athletes’ achieved performance success at least four years later (U13‒U18). (3) Results: All four prediction methods showed a medium-to-high prognostic validity with respect to their forecasts. Their values of relative improvement over chance ranged from 0.447 (logistic regression) to 0.654 (tennis recommendation score). (4) Conclusions: However, the best results are only obtained by combining the non-linear method (neural network) with one of the linear methods. Nevertheless, 18.75% of later high-performance tennis players could not be predicted using any of the methods.

Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash

In sport science, athlete tracking and motion analysis are essential for monitoring and optimizing training programs, with the goal of increasing success in competition and preventing injury. At present, contact-free, camera-based, multi-athlete detection and tracking have become a reality, mainly due to the advances in machine learning regarding computer vision and, specifically, advances in artificial convolutional neural networks (CNN), used for human pose estimation (HPE-CNN) in image sequences. Sport science in general, as well as coaches and athletes in particular, would greatly benefit from HPE-CNN-based tracking, but the sheer amount of HPE-CNNs available, as well as their complexity, pose a hurdle to the adoption of this new technology. It is unclear how many HPE-CNNs which are available at present are ready to use in out-of-the-box inference to squash, to what extent they allow motion analysis and if detections can easily be used to provide insight to coaches and athletes. Therefore, we conducted a systematic investigation of more than 250 HPE-CNNs. After applying our selection criteria of open-source, pre-trained, state-of-the-art and ready-to-use, five variants of three HPE-CNNs remained, and were evaluated in the context of motion analysis for the racket sport of squash. Specifically, we are interested in detecting player's feet in videos from a single camera and investigated the detection accuracy of all HPE-CNNs. To that end, we created a ground-truth dataset from publicly available squash videos by developing our own annotation tool and manually labeling frames and events. We present heatmaps, which depict the court floor using a color scale and highlight areas according to the relative time for which a player occupied that location during matchplay. These are used to provide insight into detections. Finally, we created a decision flow chart to help sport scientists, coaches and athletes to decide which HPE-CNN is best for player detection and tracking in a given application scenario.

Connection between Social Capital and Sport Success of Young Tennis Players

Young athletes are influenced by different physical, psychological, and social factors. Social factors significantly impact a young athlete’s growth. Parents, coaches, and schools are important factors in young athletes’ sports careers. Achieving sport success without their support would be a real challenge. Social capital is a resource that comes from social relationships and social networks. It is a resource that impacts athletes and sports performance. The aim of this study was to determine the connection between social capital and competitive success in young tennis players. This research was conducted with participants of an ITF (International Tennis Federation) junior tournament in tennis. Research included 75 tournament players (N = 36 girls, age: 15.54 ± 1.29 years; N = 39 boys, age: 16.13 ± 0.98 years). Participants filled out a questionnaire which evaluated their social capital. Social capital predictors were significant predictors of sporting success (13.1% variance explained), which indicated that there is a moderate association between social capital indicators and sport success in young tennis players. Sports performance was higher with a higher degree of family and sports team social capital among girls. It was higher with a lower school social capital among boys. Intervention that leverages social capital might serve as an avenue for performance promotion in youth.

Can human posture and range of motion be measured automatically by smart mobile applications?

Human posture and Range of Motion (ROM) are important components of a physical assessment and, from the collected data, it is possible to identify postural deviations such as scoliosis or joint and muscle limitations, hence identifying risks of more serious injuries. Posture assessment and ROM measures are also necessary metrics to monitor the effect of treatments used in the motor rehabilitation of patients, as well as to monitor their clinical progress. These evaluation processes are more frequently performed through visual inspection and manual palpation, which are simple and low cost methods. These methods, however, can be optimized with the use of tools such as photogrammetry and goniometry. Mobile solutions have also been developed to help health professionals to capture more objective data and with less risk of bias. Although there are already several systems proposed for assessing human posture and ROM in the literature, they have not been able to automatically identify and mark Anatomical and Segment Points (ASPs). The hypothesis presented here considers the development of a mobile application for automatic identification of ASPs by using machine learning algorithms and computer vision models associated with technologies embedded in smartphones. From ASPs identification, it will be possible to identify changes in postural alignment and ROM. In this context, our view is that an application derived from the hypothesis will serve as an additional tool to assist in the physical assessment process and, consequently, in the diagnosis of disorders related to postural and movement changes.