Challenging Conventional Paradigms in Applied Sports Biomechanics Research

Markerless motion capture in sport: panacea or Pandora's box?

This paper critically appraises the utility of markerless motion capture in the context of skill development and sports performance enhancement. If markerless motion capture is to be useful in a profiling or monitoring capacity, the amount of measurement error needs to be smaller than the amount of movement variability over iterative performance trials. Current evidence suggests that it is unlikely that any existing markerless system satisfies this requirement. The resolution of this issue would, in principle, enable technical errors to be more easily pinpointed and performance variation to be better explained. However, decomposing movement variability into its functional and dysfunctional components is not only a nontrivial undertaking for most practitioners, the practical consequences of doing so are limited because synergies are self-organising entities and any attempt to micro-manage their constituent degrees of freedom will likely have deleterious effects on performance. Biomechanical measurements technologies, including markerless motion capture, may be best used to support search strategies employed by coaches and athletes.

Defining movement strategies in soccer instep kicking using the relationship between pelvis and kick leg rotations

Growing evidence suggests skilled ball kickers use distinct pelvis and kick leg strategies to achieve successful performance. However, since the interaction between different strategies remains unexplored, the aims of this study were to a) examine relationships between pelvis and kick leg rotations in male players performing soccer instep kicks and b) classify different 'types' of kickers based on the observed movement strategies. Twenty semi-professional players performed kicks for maximal speed and accuracy, and kick leg and pelvis kinematics were analysed using 3D motion capture (1000 Hz). A strong relationship was found between change in pelvis transverse angular velocity and thigh-knee angular velocity ratio upon ball contact (r = 0.76, p < 0.001), and participants were categorised by their location on kick leg (thigh-knee) and pelvis (maintainer-reverser) continuums. Knowledge of a player's preferred strategy can inform departure from 'one size fits all' technical and conditioning training practices towards more individualised approaches. For example, pelvis maintainer-thigh dominant kickers might benefit from focus towards the concentric capabilities of the hip flexors, whereas reverser-knee dominant kickers might benefit from developing the ability to decelerate the pelvis and thigh to induce motion-dependent angular acceleration of the lower leg towards the ball.

An ecological-dynamical approach to golf science: implications for swing biomechanics, club design and customisation, and coaching practice

It has previously been argued that science has only made a limited contribution to the sport of golf, particularly the human element. This lack of impact could, in part, be attributed to the absence of an appropriate theoretical framework in most empirical investigations of the golf swing. This position paper outlines an ecological-dynamical approach to golf science that is better able to capture the interactions among the many structural parts of a golfer, and the relations between a golfer, his or her equipment, and his or her surrounding environment than other theoretical approaches have hitherto. It is proposed that the conjoining of principles and concepts of ecological psychology and dynamical systems theory could make a significant contribution to the enhancement of knowledge and understanding of swing biomechanics, club design and customisation, and coaching practice. This approach could also provide a platform on which to integrate the various subdisciplines of sport and human movement science to gain a more holistic understanding of golf performance.

The Influence of Dietary Carbohydrate on Perceived Recovery Status Differs at the Group and Individual Level—Evidence of Nonergodicity Among Endurance Athletes

Purpose

Research findings are typically reported at the group level but applied to individuals. However, an emerging issue in sports science concerns nonergodicity—whereby group-level data cannot be generalized to individuals. The purpose of this study was to determine if the relationship between daily carbohydrate intake and perceived recovery status displays nonergodicity.

Methods

Fifty-five endurance athletes recorded daily measures of self-selected dietary intake, training, sleep, and subjective wellbeing for 12 weeks. We constructed linear models to measure the influence of daily carbohydrate intake on perceived recovery status while accounting for training load, sleep duration, sleep quality, and muscle soreness. Using linear model coefficients for carbohydrate intake we tested whether the distributions (mean and SD) differed at the group and individual levels (indicating nonergodicity). Additionally, a decision tree was created to explore factors that could provide an indication of an individual athlete’s relationship between carbohydrate intake and perceived recovery status.

Results

Mean values were not different between group- and individual-level analyses, but SDs at the individual level were ~2.4 times larger than at the group level, indicating nonergodicity. Model coefficients for carbohydrate intake were negative for three participants, positive for four participants, and non-significant for 37 participants. The κ value measuring accuracy of the decision tree was 0.52, indicating moderate prediction accuracy.

Conclusion

For most individuals, carbohydrate intake did not influence recovery status. However, the influence of dietary carbohydrate intake on daily recovery differs at the group and individual level. Therefore, practical recommendations should be based on individual-level analysis.

An ecological dynamics approach to ACL injury risk research: a current opinion

Research of non-contact anterior cruciate ligament (ACL) injury risk aims to identify modifiable risk factors that are linked to the mechanisms of injury. Information from these studies is then used in the development of injury prevention programmes. However, ACL injury risk research often leans towards methods with three limitations: 1) a poor preservation of the athlete-environment relationship that limits the generalisability of results, 2) the use of a strictly biomechanical approach to injury causation that is incomplete for the description of injury mechanisms, 3) and a reductionist analysis that neglects profound information regarding human movement. This current opinion proposes three principles from an ecological dynamics perspective that address these limitations. First, it is argued that, to improve the generalisability of findings, research requires a well-preserved athlete-environment relationship. Second, the merit of including behaviour and the playing situation in the model of injury causation is presented. Third, this paper advocates that research benefits from conducting non-reductionist analysis (i.e., more holistic) that provides profound information regarding human movement. Together, these principles facilitate an ecological dynamics approach to injury risk research that helps to expand our understanding of injury mechanisms and thus contributes to the development of preventative measures.

Advancing mental imagery research from an interdisciplinary sport science perspective: a commentary on Frank et al. (2023)

Frank et al.'s (2023) perceptual-cognitive scaffold meaningfully extends the cognitive action architecture approach and we support this interdisciplinary advancement. However, there are theoretical and applied aspects that could be further developed within this research to maximise practical impact across domains such as sport. In particular, there is a need to consider how these mechanisms (1) might critically inform or relate to other prominent theories within sport (e.g., constrained action hypothesis and ecological approaches) and, (2) reflect the real-world challenges experienced by athletes. With these ideas in mind, this commentary aims to stimulate discussion and enhance the translational application of Frank et al.'s research.

Inter- and intra-athlete technique variability of conventional new ball swing bowling in elite and pre-elite Australian male fast bowlers

This study aimed to investigate inter- and intra-athlete technique variability in pre-elite and elite Australian fast bowlers delivering new ball conventional swing bowling. Ball grip angle and pelvis, torso, shoulder, elbow, wrist, upper arm, forearm, and hand kinematics were investigated at the point of ball release for inswing and outswing deliveries. Descriptive evaluations of group and individual data and k-means cluster analyses were used to assess inter- and intra-bowler technique variability. Inter-athlete technique and ball grip variability were identified, demonstrating that skilled bowlers use individualised strategies to generate swing. Functional movement variability was demonstrated by intra-athlete variability in successful swing bowling trials. Bowlers demonstrated stable technique parameters in large proximal body segments of the pelvis and torso, providing a level of repeatability to their bowling action. Greater variation was observed in bowling arm kinematics, allowing athletes to manipulate the finger and ball position to achieve the desired seam orientation at the point of ball release. This study demonstrates that skilled bowlers use individualised techniques and grips to generate swing and employ technique variations in successive deliveries. Coaches should employ individualised training strategies and use constraints-led approaches in training environments to encourage bowlers to seek adaptive movement solutions to generate swing.

Optimal initial position and technique for the front foot contact phase of cricket fast bowling: Commonalities between individual-specific simulations of elite bowlers

Group-based and individual-based studies in cricket fast bowling have identified common technique characteristics associated with ball release speed. The applicability of these findings to individual bowlers is often questioned, however, due to research approach limitations. This study aims to identify whether the optimal initial body position at front foot contact and subsequent technique to maximise ball release speed exhibit common characteristics for elite male cricket fast bowlers using individual-specific computer optimisations. A planar 16-segment whole-body torque-driven simulation model of the front foot contact phase of fast bowling was customised, evaluated, and the initial body position and subsequent movement pattern optimised, for ten elite male fast bowlers. The optimised techniques significantly increased ball release speed by 4.8 ± 1.3 ms-1 (13.5 ± 4.1%) and ranged between 37.8 and 42.9 ms-1, and in lower peak ground reaction forces and loading rates. Common characteristics were observed within the optimal initial body position with more extended front knees, as well as more flexion of the front and bowling arm shoulders than in current performances. Delays to the onset of trunk flexion, front arm and bowling arm shoulder extension, and wrist flexion were also common in the subsequent movement during the front foot contact phase. Lower front hip extensor and front shoulder flexor torques, as well as greater bowling shoulder extensor torques were also evident. This is useful knowledge for coach development, talent identification, and coaching practice.

Exercise Biomechanics for Health: Evaluating Lifelong Activities for Well-Being

Biomechanics is a multidisciplinary study of the mechanical laws and principles that govern human movement and the functioning of biological systems [...].

Applications of regularized regression models in sports biomechanics research

Research in sports biomechanics often relies on the use of ordinary least squares (OLS) regression. However, since sports biomechanics research is often characterised by high-dimensional data sets with many predictor variables and few observations, use of OLS regression can sometimes be problematic from a statistical perspective. Statistical learning methods may provide alternate ways to deal with high-dimensional data sets and partially address these problems. For example, regularisation adds penalties to the cost function of OLS regression models, which shrinks large regression coefficients and decreases the model's sensitivity to noise in the data. Regularised regression models also protect against overfitting, improve generalisability, and can be used for variable selection. A short review of biomechanics research studies illustrates how these models provided ways to reduce the number of variables within a model and select only the primary predictors of performance, which helped with the interpretation of results and identified distinct combinations of key predictors of performance. In addition, we illustrate how these models are applied to two sports biomechanics datasets. Given the advantages, sports biomechanists may want to consider the use of regularised regression models in their research design and statistical analyses. Careful consideration should be given, however, to the construction, validation, and interpretation of these models considering their underpinning assumptions and limitations.

Resilience in sports: a multidisciplinary, dynamic, and personalized perspective

Athletes are exposed to various psychological and physiological stressors, such as losing matches and high training loads. Understanding and improving the resilience of athletes is therefore crucial to prevent performance decrements and psychological or physical problems. In this review, resilience is conceptualized as a dynamic process of bouncing back to normal functioning following stressors. This process has been of wide interest in psychology, but also in the physiology and sports science literature (e.g. load and recovery). To improve our understanding of the process of resilience, we argue for a collaborative synthesis of knowledge from the domains of psychology, physiology, sports science, and data science. Accordingly, we propose a multidisciplinary, dynamic, and personalized research agenda on resilience. We explain how new technologies and data science applications are important future trends (1) to detect warning signals for resilience losses in (combinations of) psychological and physiological changes, and (2) to provide athletes and their coaches with personalized feedback about athletes' resilience.

Assessing biomechanics and associated factors in individuals with patellofemoral pain in a clinical setting: A qualitative study based on interviews with expert clinicians

OBJECTIVES

To explore the perspectives of ten clinicians from different medical disciplines with experience in managing PFP on how to conduct biomechanical assessments in individuals with PFP in a clinical setting.

METHODS

An explorative qualitative design was used to explore the perspectives of ten clinicians with at least five years of experience managing patients with patellofemoral pain. A series of semi-structured interviews were done over Zoom video chat. The participants were from six different medical disciplines (physiotherapy, biokinetics, podiatry, sport science, sports medicine, orthopaedic surgery). Data was analysed thematically.

RESULTS

Four main themes emerged from the data. These were: 1) biomechanical contributing factors that clinicians routinely screen for in patients with PFP; 2) relevant functional activities for biomechanical screening in patients with PFP; 3) conducting gait analysis assessments in a clinical setting; and 4) challenges of biomechanical assessment in patients with PFP. The clinicians expressed conflicting opinions on the usefulness of clinical gait analysis. The clinicians questioned the clinical applicability of some of the biomechanical factors identified as important in the evidence.

CONCLUSIONS

It is important for clinicians to include the biomechanical assessment of functional activities linked to pain when managing patients with PFP. However, some of the biomechanical factors identified in the evidence are too difficult to observe without 3D movement analysis equipment and should not be considered clinically relevant. Expert clinical opinion is important to provide contextual information when addressing biomechanics in individuals with PFP.

Factors influencing the jump momentum - sprint momentum correlation: a data simulation

Jump take-off momentum has previously been proposed as an alternative test to predict sprint momentum. This study used a data simulation to replicate and systematically investigate relationships reported in previous studies between body mass, vertical jump performance, and sprint performance. Results were averaged for 1000 simulated data sets in each condition, and the effects of various parameters on correlations between jump momentum and sprint momentum were determined. The ability of jump take-off momentum to predict sprint momentum is greatest under relatively high inter-individual variation in body mass and relatively low inter-individual variation in jump height. This is largely due to the increased emphasis on body mass in these situations. Even under zero or a small negative (r = -0.30) correlation between jump height and sprint velocity, the correlation between the two momenta remained very large (r ≥ 0.76) on average. There were no investigated conditions under which jump momentum was most frequently a significantly (p < 0.05) greater predictor of sprint momentum compared to simply using body mass alone. Furthermore, between-individual correlations should not be used to make inferences or predictions for within-individual applications (e.g. predicting or evaluating the effects of a longitudinal training intervention). It is recommended that any rationale for calculating and/or monitoring jump take-off momentum should be separate from its ability to predict sprint momentum. Indeed, body mass alone may be a better predictor of sprint momentum.

Nonergodicity in Load and Recovery: Group Results Do Not Generalize to Individuals

PURPOSE

The study of load and recovery gained significant interest in the last decades, given its important value in decreasing the likelihood of injuries and improving performance. So far, findings are typically reported on the group level, whereas practitioners are most often interested in applications at the individual level. Hence, the aim of the present research is to examine to what extent group-level statistics can be generalized to individual athletes, which is referred to as the "ergodicity issue." Nonergodicity may have serious consequences for the way we should analyze, and work with, load and recovery measures in the sports field.

METHODS

The authors collected load, that is, rating of perceived exertion × training duration, and total quality of recovery data among youth male players of a professional football club. This data were collected daily across 2 seasons and analyzed on both the group and the individual level.

RESULTS

Group- and individual-level analysis resulted in different statistical outcomes, particularly with regard to load. Specifically, SDs within individuals were up to 7.63 times larger than SDs between individuals. In addition, at either level, the authors observed different correlations between load and recovery.

CONCLUSIONS

The results suggest that the process of load and recovery in athletes is nonergodic, which has important implications for the sports field. Recommendations for training programs of individual athletes may be suboptimal, or even erroneous, when guided by group-level outcomes. The utilization of individual-level analysis is key to ensure the optimal balance of individual load and recovery.

Characterising initial sprint acceleration strategies using a whole-body kinematics approach

Sprint acceleration is an important motor skill in team sports, thus consideration of techniques adopted during the initial steps of acceleration is of interest. Different technique strategies can be adopted due to multiple interacting components, but the reasons for, and performance implications of, these differences are unclear. 29 professional rugby union backs completed three maximal 30 m sprints, from which spatiotemporal variables and linear and angular kinematics during the first four steps were obtained. Leg strength qualities were also obtained from a series of strength tests for 25 participants, and 13 participants completed the sprint protocol on four separate occasions to assess the reliability of the observed technique strategies. Using hierarchical agglomerative cluster analysis, four clear participant groups were identified according to their normalised spatiotemporal variables. Whilst significant differences in several lower limb sprint kinematic and strength qualities existed between groups, there were no significant between-group differences in acceleration performance, suggesting inter-athlete technique degeneracy in the context of performance. As the intra-individual whole-body kinematic strategies were stable (mean CV = 1.9% to 6.7%), the novel approach developed and applied in this study provides an effective solution for monitoring changes in acceleration technique strategies in response to technical or physical interventions.

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.

Beyond animated skeletons: How can biomechanical feedback be used to enhance sports performance?

Biomechanical feedback technologies are becoming increasingly prevalent in elite athletic training environments but how the kinematic and kinetic data they produce can be best used to improve sports techniques and enhance sports performance is unclear. This paper draws on theoretical and empirical developments in the motor control, skill acquisition, and sports biomechanics literatures to offer practical guidance and strategic direction on this issue. It is argued that the information produced by biomechanical feedback technologies can only describe, with varying degrees of accuracy, what patterns of coordination and control are being adopted by the athlete but, crucially, it cannot prescribe how these patterns of coordination and control should be modified to enhance sports performance. As conventional statistical and theoretical modelling paradigms in applied sports biomechanics provide limited information about patterns of coordination and control, and do not permit the identification of athlete-specific optimum sports techniques, objective criteria on which to base technical modifications that will consistently lead to enhanced performance outcomes cannot reliably be established for individual athletes. Given these limitations, an alternative approach, which is harmonious with the tenets of dynamical systems theory and aligned with the pioneering insights of Bernstein (1967) on skill acquisition, is advocated. This approach involves using kinematic and kinetic data to channel the athlete's search towards their own unique 'optimum' pattern of coordination and control as they actively explore their perceptual-motor workspace during practice. This approach appears to be the most efficacious use of kinematic and kinetic data given current biomechanical knowledge about sports techniques and the apparent inability of existing biomechanical modelling approaches to accurately predict how technique changes will impact on performance outcomes for individual athletes.

Individuals with gluteal tendon repair display similar hip biomechanics to those of a healthy cohort during a sit-to-stand task

BACKGROUND

Gluteal-tendon repair (GTR) is reported to be effective for relieving pain and improving clinical function in patients with gluteal-tendon tears. The sit-to-stand (STS) task is an important activity of daily living and is often used to assess functional capacity in clinical populations. Understanding if and how STS performance is altered in individuals with gluteal tendon repair may be an effective marker of GTR outcomes as well as a possible therapeutic target for post-operative rehabilitation.

RESEARCH QUESTION

Do biomechanical parameters during STS differ between age- and sex-matched participants with and without gluteal-tendon repair?

METHODS

27 participants with a GTR and 29 healthy participants performed the STS task. Data were acquired using the three-dimensional motion capture system and forceplates. Outcomes of interest were task duration, rate of force development, trunk, pelvis, and hip joint angles, moments and powers. Differences were assessed using Generalised linear multivariate models and statistical parametric mapping.

RESULTS

GTR patients performed the STS movement significantly slower (1.4+/- 0.40 s) compared to controls (1.1+/ -0.2 s) with a significantly lower rate of force development (35.1+/- 5.7 N/kg/ms vs 30.3+/- 8.5 N/kg/ms). There were no group differences for hip, pelvis, or trunk angle over the movement cycle or for maximal or minimal values. Furthermore, there were no significant differences detected in hip joint kinetics. However, there appeared to be substantial between-subject variability indicating different patient-specific movements patterns.

SIGNIFICANCE

Individuals with a GTR performed the STS task about 20 % slower than healthy controls with a lower rate of force development. The individual variations indicate that participants likely employed different movement strategies to achieve STS. While the lack of differences between groups could suggest that GTR helps restore function and corrects the proposed underlying aetiology, it is possible that the STS task was not sufficiently challenging to discriminate between groups.

Application of VR image recognition and digital twins in artistic gymnastics courses

Because rhythmic gymnastics requires a combination of human movements and hand-held instruments, it is difficult to teach and requires high movement standards. Therefore, the actual course teaching is difficult. In order to improve the teaching efficiency of rhythmic gymnastics courses, based on VR image recognition technology and digital twins, this paper combines the actual teaching needs of rhythmic gymnastics to build a corresponding auxiliary teaching system. The sports database designed in this article mainly has three kinds of sports: difficulty movements, connecting movements and equipment movements. It is different from the traditional method in that each movement and the device-related connection movement correspond to a difficulty movement of the same length and close coordination, and the connection movement plays a role in smoothly connecting the two difficulty movements. In addition, the performance of the auxiliary teaching system constructed in this paper is studied through system experiments. The research results show that this system is feasible.

Assessing Technical Skills in Talented Youth Athletes: A Systematic Review

Background

Talent identification and development (TID) programs aim to identify players with the greatest potential for long-term success. Previous research suggests that the assessment of sport-specific technical skills is valuable for discriminating between more and less skilled individuals and/or for predicting future performance.

Objective

This review aims to provide an overview on both the instruments used to assess sport-specific technical skills and their discriminatory, explanatory and/or predictive findings in the context of TID.

Methods

Electronic searches were conducted in PubMed, Web of Knowledge, SPORTDiscus, SURF and Scopus (January 1990–October 2019). Search terms covered the areas of sport, technical skills assessment, performance, skill level and youth. In the end, 59 relevant studies were identified and evaluated.

Results

The results highlight the widespread and important role of technical skills in TID; almost all studies (93%) reported discriminatory, explanatory and/or predictive benefits for the assessment of sport-specific technical skills. Analyzing and categorizing the number of assessment methods applied in the studies (n = 69) according to their method type (‘technique-related’ or ‘outcome-related’ variables) and method set-up (‘experimental’ or ‘competition’ data acquisition environment) indicated a clear tendency towards ‘outcome-related’ (73%) and ‘experimental’ (75%) assessment methods. We also found a strong overrepresentation of studies assessing cross-sectional data (75%) in soccer (53%) in male samples (74% of studies reporting subjects’ sex) from European countries (64%).

Conclusions

On the one hand, our findings demonstrate the great capability of sport-specific technical skills assessments to discriminate different performance levels and predict future performance in TID activities. On the other hand, this review highlights the focus on ‘outcome-related’ and ‘experimental’ methods in specific populations and, consequently, the limited knowledge in other areas. Here, the application of ‘technique-related’ and ‘competition’ methods appears promising for adding new knowledge, especially in the light of technological advances.

Development of Machine Learning Algorithms for the Determination of the Centre of Mass

The study of the human body and its movements is still a matter of great interest today. Most of these issues have as their fulcrum the study of the balance characteristics of the human body and the determination of its Centre of Mass. In sports, a lot of attention is paid to improving and analysing the athlete’s performance. Almost all the techniques for determining the Centre of Mass make use of special sensors, which allow determining the physical magnitudes related to the different movements made by athletes. In this paper, a markerless method for determining the Centre of Mass of a subject has been studied, comparing it with a direct widely validated equipment such as the Wii Balance Board, which allows determining the coordinates of the Centre of Pressure. The Motion Capture technique was applied with the OpenPose software, a Computer Vision method boosted with the use of Convolution Neural Networks. Ten quasi-static analyses have been carried out. The results have shown an error of the Centre of Mass position, compared to that obtained from the Wii Balance Board, which has been considered acceptable given the complexity of the analysis. Furthermore, this method, despite the traditional methods based on the use of balances, can be used also for prediction of the vertical position of the Centre of Mass.

A Review of Forward-Dynamics Simulation Models for Predicting Optimal Technique in Maximal Effort Sporting Movements

The identification of optimum technique for maximal effort sporting tasks is one of the greatest challenges within sports biomechanics. A theoretical approach using forward-dynamics simulation allows individual parameters to be systematically perturbed independently of potentially confounding variables. Each study typically follows a four-stage process of model construction, parameter determination, model evaluation, and model optimization. This review critically evaluates forward-dynamics simulation models of maximal effort sporting movements using a dynamical systems theory framework. Organismic, environmental, and task constraints applied within such models are critically evaluated, and recommendations are made regarding future directions and best practices. The incorporation of self-organizational processes representing movement variability and “intrinsic dynamics” remains limited. In the future, forward-dynamics simulation models predicting individual-specific optimal techniques of sporting movements may be used as indicative rather than prescriptive tools within a coaching framework to aid applied practice and understanding, although researchers and practitioners should continue to consider concerns resulting from dynamical systems theory regarding the complexity of models and particularly regarding self-organization processes.

An artificial intelligence and machine vision based evaluation of physical education teaching

The manual evaluation method to evaluate the effect of physical education teaching is tedious, and it will have a large error when the amount of data is large. In order to improve the efficiency of physical education evaluation, this article uses artificial intelligence for data analysis and uses machine vision to identify the teaching process to assist teachers in physical education. In order to reduce the calibration error of the parameters and obtain more accurate camera imaging geometric parameters, this paper adopts the method of averaging multiple sample points to determine the calibration parameters of the camera. In addition, this study builds system function modules according to actual needs and verifies system performance through experimental teaching methods. The research results show that the model proposed in this paper has a certain practical effect.

Role of artificial intelligence algorithm for taekwondo teaching effect evaluation model

The problems and disadvantages of the traditional teaching mode of Taekwondo in colleges and universities are obvious, which is not conducive to cultivating the interest of contemporary college students in learning Taekwondo. In order to improve the teaching effect of Taekwondo, based on the intelligent algorithm of human body feature recognition, this study uses support vector machine to construct a Taekwondo teaching effect evaluation model based on artificial intelligence algorithm. The model corrects the movement of the students by recognizing the movement characteristics of the students’ Taekwondo and can conduct the movement guidance and exercises through the simulation method. In order to verify the performance of the model in this study, this study set up control experiments and mathematical statistical methods to verify the performance of the model. The research results show that the model proposed in this paper has a certain effect and can be applied to teaching practice

Fatigue-Related and Timescale-Dependent Changes in Individual Movement Patterns Identified Using Support Vector Machine

The scientific and practical fields-especially high-performance sports-increasingly request a stronger focus be placed on individual athletes in human movement science research. Machine learning methods have shown efficacy in this context by identifying the unique movement patterns of individuals and distinguishing their intra-individual changes over time. The objective of this investigation is to analyze biomechanically described movement patterns during the fatigue-related accumulation process within a single training session of a high number of repeated executions of a ballistic sports movement-specifically, the frontal foot kick (mae-geri) in karate-in expert athletes. The two leading research questions presented for consideration are (1) Can characteristics of individual movement patterns be observed throughout the entire training session despite continuous changes, i.e., even as fatigue-related processes increase? and (2) How do intra-individual movement patterns change as fatigue-related processes increase throughout a training session? Sixteen expert karatekas performed 606 frontal foot kicks directed toward an imaginary target. The kicks were performed in nine sets at 80% (K-80) of the self-experienced maximal intensity. In addition, six kicks at maximal intensity (K-100) were performed after each of the nine sets. Between the sets, the participants took a 90-s break. Three-dimensional full-body kinematic data of all kicks were recorded with 10 infrared cameras. The normalized waveforms of nine upper- and lower-body joint angles were classified using a supervised machine learning method (support vector machine). The results of the classification revealed a disjunct distinction between the kinematic movement patterns of individual athletes. The identification of unique movement patterns of individual athletes was independent of the intensity and the degree of fatigue-related processes. In other words, even with the accumulation of fatigue-related processes, the unique movement patterns of an individual athlete can be clearly identified. During the training session, changes in intra-individual movement patterns could also be detected, indicating the occurrence of adaptations in individual movement patterns throughout the fatigue-related accumulation process. The results suggest that these adaptations can be modeled in terms of changes in patterns rather than increasing variance. Practical consequences are critically discussed.

Quantifying bi-variate coordination variability during longitudinal motor learning of a complex skill

Biofeedback (BFb) can enhance the motor learning process by guiding skill exploration. Too much BFb, however, can foster dependency leading to skill retention deficits once removed. A reducing BFb schedule could negate dependency effects, however limited methodologies exist to assess the effectiveness of an intervention during application. This research proposes a new bi-variate method (CI2_Area) to quantify coordination variability (Coord_Var) as a measure of skill exploration during a motor learning intervention. Thirty-two participants were introduced to a novel explosive-lunge task. A BFb group (n = 16) were provided with visual BFb on rear hip, knee and ankle joint extension magnitudes and timing during a 26-week reducing schedule BFb intervention. Coord_Var of hip-knee and knee-ankle angular velocities were quantified by calculating the area encompassed by the 95% confidence intervals of joint coupling angular-velocity bi-variate plots (CI2_Area). Linear regressions were fitted to group and individual Coord_Var longitudinal data. The BFb was effective in successfully altering whole limb technique within just two sessions, and these changes were retained. The BFb group demonstrated a continual increase of Coord_Var throughout the intervention, showing continual skill exploration strategies, while the Control group remained unchanged. Gradually increasing time between sessions, using a longitudinally reducing BFb schedule, successfully negates dependency effects on BFb while also encouraging motor learning. Manipulating time between sessions allows for the provision of a high frequency of 100% BFb without fostering dependency. The CI2_Area method was able to detect individual exploration strategies and could be used in the future to direct individual intervention modifications.

In search of sports biomechanics' holy grail: Can athlete-specific optimum sports techniques be identified?

The development of methods that can identify athlete-specific optimum sports techniques-arguably the holy grail of sports biomechanics-is one of the greatest challenges for researchers in the field. This 'perspectives article' critically examines, from a dynamical systems theoretical standpoint, the claim that athlete-specific optimum sports techniques can be identified through biomechanical optimisation modelling. To identify athlete-specific optimum sports techniques, dynamical systems theory suggests that a representative set of organismic constraints, along with their non-linear characteristics, needs to be identified and incorporated into the mathematical model of the athlete. However, whether the athlete will be able to adopt, and reliably reproduce, his/her predicted optimum technique will largely be dependent on his/her intrinsic dynamics. If the attractor valley corresponding to the existing technique is deep, or if the attractor valleys corresponding to the existing technique and the predicted optimum technique are in different topographical regions of the dynamic landscape, technical modifications may be challenging or impossible to reliably implement even after extended practice. The attractor layout defining the intrinsic dynamics of the athlete, therefore, needs to be determined to establish the likelihood of the predicted optimum technique being reliably attainable by the athlete. Given the limited set of organismic constraints typically used in mathematical models of athletes, combined with the methodological challenges associated with mapping the attractor layout of an athlete, it seems unlikely that athlete-specific optimum sports techniques will be identifiable through biomechanical optimisation modelling for the majority of sports skills in the near future.