Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review

Pacing Strategies in Elite Individual-Medley Swimmers: A Decision-Tree Approach

PURPOSE

This study aimed to examine pacing strategies and identify the stroke that has the most significant impact on overall performance in men's and women's 200-m and 400-m individual-medley events from 2000 to 2021.

METHODS

The time in each lap and overall race was retrieved from the World Aquatics website. The standardized time for each stroke in individual medley was calculated by dividing the actual time by a reference time specific to each stroke. The reference time was derived from the respective laps in single-stroke finals in the 2017 World Swimming Championships. The decision-tree method was used for analysis. The dependent variables were qualified or nonqualified in heats and semifinals, and winning medals in finals. The independent variables were the ratio of standardized time in each stroke to the sum of standardized time in all 4 strokes.

RESULTS

Swimmers who spent a higher ratio of standardized time in the butterfly stroke (>0.236-0.245) are associated with a higher likelihood of winning medals or qualifying for the next stage in most men's and women's 200-m and 400-m individual medley. Butterfly exhibited the highest normalized importance that distinguished medalists from nonmedalists in the finals. The front-crawl stroke is the second most important determinant in medalists in men's and women's 200-m individual medley, whereas backstroke and breaststroke were the second most important in men's and women's 400-m individual medley, respectively.

CONCLUSION

Individual-medley swimmers who were excellent in butterfly and conserved energy in butterfly had a higher likelihood of success.

The influence of psychological change factors of tennis training strategy using optimized recurrent neural network and artificial intelligence

Due to the specialization of tennis technical training, the teaching focus of tennis teaching has gradually shifted to the psychological skills training of tennis players. This work addresses the impact of psychological factors on tennis players' insufficient concentration in teaching and training on match results. It discusses the psychological changes' influencing factors in tennis training strategies and analyzes the current psychological changes that are easy to occur in tennis teaching. The Recurrent Neural Network (RNN) can simulate the human brain's information processing and learning process to establish models to study human psychological changes. To explore the influence of psychological changes on tennis training, artificial intelligence technology is combined to optimize the performance of RNN, and a prediction model of psychological distress in tennis training is constructed. Additionally, a questionnaire is applied to compare the sports state of tennis players before and after the psychological regulation intervention. The findings demonstrate that following psychological regulation, 73 % of players perform as usual, 20 % present exceptionally well, and 7 % do not perform as well as usual. These results indicate an improvement compared to previous performances, highlighting the efficacy of psychological regulation supported by optimized RNN under AI assistance. This study aims to foster a consistently positive psychological state among tennis players during daily training and competitions, ensuring that their competitive performance levels remain normal or even exceed their usual standards.

Application of Artificial Intelligence in rehabilitation science: A scientometric investigation Utilizing Citespace

This study presents a scientometric analysis of the intersection between rehabilitation science and artificial intelligence (AI) technologies, using data from the Web of Science (WOS) database from 2002 to 2022. The analysis employed a comprehensive search query with key AI-related terms, focusing on a wide range of publications in rehabilitation science. Utilizing the Citespace tool, the study visualizes and quantifies the relationships between key terms, identifies research trends, and assesses the impact of AI technologies in rehabilitation science. Findings reveal a significant increase in AI-related research in this field, particularly from 2017 onwards, peaking in 2021. The United States has been a leading contributor, followed by countries like England, Australia, Germany, and Canada. Major institutional contributions come from Harvard University and the Pennsylvania Commonwealth System of Higher Education, among others. A keyword co-occurrence network constructed through Citespace identifies nine distinct hot topics and various research frontiers, highlighting evolving focus areas within the field. Burst analysis of keywords indicates a shift from performance and injury-related research to an increasing emphasis on AI and deep learning in recent years. The study also predicts the potential impact of papers, spotlighting works by Kunze KN and others as significantly influencing future research directions. Additionally, it examines the evolution of knowledge bases in AI-related rehabilitation science research, revealing a multidisciplinary core that includes neurology, rehabilitation, and ophthalmology, extending to complementary fields such as medicine and social sciences. This scientometric analysis provides a comprehensive overview of AI's application in rehabilitation science, offering insights into its evolution, impact, and emerging trends over the past two decades. The findings suggest strategic directions for future research, policy-making, and interdisciplinary collaboration in rehabilitation science and AI.

Identifying Key Training Load and Intensity Indicators in Ice Hockey Using Unsupervised Machine Learning

To identify key training load (TL) and intensity indicators in ice hockey, practice, and game data were collected using a wearable 200-Hz accelerometer and heart rate (HR) recording throughout a four-week (29 days) competitive period (23 practice sessions and 8 competitive games in 17 elite Danish players (n = 427 observations). Within-individual correlations among accelerometer- (total accelerations [Acctot], accelerations >2 m·s-2 [Acc2], total accelerations [Dectot], decelerations <- 2 m·s-2 [Dec2]), among HR-derived (time >85% maximum HR [t85%HRmax], Edwards' TL and modified training impulse) TL indicators, and between acceleration- and HR-derived TL parameters were large to almost perfect (r = 0.69-0.99). No significant correlations were observed between accelerometer- and HR-derived intensity indicators. Three between- and two within-components were found. The K-means++ cluster analysis revealed five and four clusters for between- and within-loadings, respectively. The least Euclidean distance from their centroid for each cluster was reported by session-duration, Acctot, Dec2, TRIMPMOD, %t85HRmax for between-loadings, whereas session-duration, Acc2, t85HRmax and Dec2/min for within-loadings. Specific TL or intensity variables might be relevant to identify similar between-subject groups (e.g. individual player, playing positions), or temporal patterns (e.g. changes in TL or intensity over time). Our study provides insights about the redundancy associated with the use of multiple TL and intensity variables in ice hockey.

SnowMotion: A Wearable Sensor-Based Mobile Platform for Alpine Skiing Technique Assistance

Skiing technique and performance improvements are crucial for athletes and enthusiasts alike. This study presents SnowMotion, a digital human motion training assistance platform that addresses the key challenges of reliability, real-time analysis, usability, and cost in current motion monitoring techniques for skiing. SnowMotion utilizes wearable sensors fixed at five key positions on the skier's body to achieve high-precision kinematic data monitoring. The monitored data are processed and analyzed in real time through the SnowMotion app, generating a panoramic digital human image and reproducing the skiing motion. Validation tests demonstrated high motion capture accuracy (cc > 0.95) and reliability compared to the Vicon system, with a mean error of 5.033 and a root-mean-square error of less than 12.50 for typical skiing movements. SnowMotion provides new ideas for technical advancement and training innovation in alpine skiing, enabling coaches and athletes to analyze movement details, identify deficiencies, and develop targeted training plans. The system is expected to contribute to popularization, training, and competition in alpine skiing, injecting new vitality into this challenging sport.

Application of Machine Learning Methods to Investigate Joint Load in Agility on the Football Field: Creating the Model, Part I

Laboratory studies have limitations in screening for anterior cruciate ligament (ACL) injury risk due to their lack of ecological validity. Machine learning (ML) methods coupled with wearable sensors are state-of-art approaches for joint load estimation outside the laboratory in athletic tasks. The aim of this study was to investigate ML approaches in predicting knee joint loading during sport-specific agility tasks. We explored the possibility of predicting high and low knee abduction moments (KAMs) from kinematic data collected in a laboratory setting through wearable sensors and of predicting the actual KAM from kinematics. Xsens MVN Analyze and Vicon motion analysis, together with Bertec force plates, were used. Talented female football (soccer) players (n = 32, age 14.8 ± 1.0 y, height 167.9 ± 5.1 cm, mass 57.5 ± 8.0 kg) performed unanticipated sidestep cutting movements (number of trials analyzed = 1105). According to the findings of this technical note, classification models that aim to identify the players exhibiting high or low KAM are preferable to the ones that aim to predict the actual peak KAM magnitude. The possibility of classifying high versus low KAMs during agility with good approximation (AUC 0.81-0.85) represents a step towards testing in an ecologically valid environment.

Comparison of Machine Learning Algorithms Fed with Mobility-Related and Baropodometric Measurements to Identify Temporomandibular Disorders

Temporomandibular disorders (TMDs) refer to a group of conditions that affect the temporomandibular joint, causing pain and dysfunction in the jaw joint and related muscles. The diagnosis of TMDs typically involves clinical assessment through operator-based physical examination, a self-reported questionnaire and imaging studies. To objectivize the measurement of TMD, this study aims at investigating the feasibility of using machine-learning algorithms fed with data gathered from low-cost and portable instruments to identify the presence of TMD in adult subjects. Through this aim, the experimental protocol involved fifty participants, equally distributed between TMD and healthy subjects, acting as a control group. The diagnosis of TMD was performed by a skilled operator through the typical clinical scale. Participants underwent a baropodometric analysis by using a pressure matrix and the evaluation of the cervical mobility through inertial sensors. Nine machine-learning algorithms belonging to support vector machine, k-nearest neighbours and decision tree algorithms were compared. The k-nearest neighbours algorithm based on cosine distance was found to be the best performing, achieving performances of 0.94, 0.94 and 0.08 for the accuracy, F1-score and G-index, respectively. These findings open the possibility of using such methodology to support the diagnosis of TMDs in clinical environments.

Process mining to investigate the relationship between clinical antecedents and injury risk, severity and return to play in professional sports

Objective

This paper presents an exploratory case study focusing on the applicability and value of process mining in a professional sports healthcare setting. We explore whether process mining can be retrospectively applied to readily available data at a professional sports club (Football Club Barcelona) and whether it can be used to obtain insights related to care flows.

Design

Our study used discovery process mining to detect patterns and trends in athletes' Post-Pre-Participation Medical Evaluation injury route, encompassing five phases for analysis and interpretation.

Results

We examined preprocessed data in event log format to determine the injury status of athletes in respective baseline groups (healthy or pathological). Our analysis found a link between thigh muscle injuries and later ankle joint problems. The process model found three loops with recurring injuries, the most common of which were thigh muscle injuries. There were no differences in injury rates or the median number of days to return to play between the healthy and pathological groups.

Conclusions

This study explored the applicability and value of process mining in a professional sports healthcare setting. We established that process mining can be retrospectively applied to readily available data at a professional sports club and that this approach can be used to obtain insights related to sports healthcare flows.

Best practices for the dissemination and implementation of neuromuscular training injury prevention warm-ups in youth team sport: a systematic review

OBJECTIVE

To evaluate best practices for neuromuscular training (NMT) injury prevention warm-up programme dissemination and implementation (D&I) in youth team sports, including characteristics, contextual predictors and D&I strategy effectiveness.

DESIGN

Systematic review.

DATA SOURCES

Seven databases were searched.

ELIGIBILITY

The literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

INCLUSION CRITERIA

participation in a team sport, ≥70% youth participants (<19 years), D&I outcomes with/without NMT-related D&I strategies. The risk of bias was assessed using the Downs & Black checklist.

RESULTS

Of 8334 identified papers, 68 were included. Sport participants included boys, girls and coaches. Top sports were soccer, basketball and rugby. Study designs included randomised controlled trials (RCTs) (29.4%), cross-sectional (23.5%) and quasi-experimental studies (13.2%). The median Downs & Black score was 14/33. Injury prevention effectiveness (vs efficacy) was rarely (8.3%) prioritised across the RCTs evaluating NMT programmes. Two RCTs (2.9%) used Type 2/3 hybrid approaches to investigate D&I strategies. 19 studies (31.6%) used D&I frameworks/models. Top barriers were time restrictions, lack of buy-in/support and limited benefit awareness. Top facilitators were comprehensive workshops and resource accessibility. Common D&I strategies included Workshops with supplementary Resources (WR; n=24) and Workshops with Resources plus in-season Personnel support (WRP; n=14). WR (70%) and WRP (64%) were similar in potential D&I effect. WR and WRP had similar injury reduction (36-72%) with higher adherence showing greater effectiveness.

CONCLUSIONS

Workshops including supplementary resources supported the success of NMT programme implementation, however, few studies examined effectiveness. High-quality D&I studies are needed to optimise the translation of NMT programmes into routine practice in youth sport.

Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives

BACKGROUND

The development and application of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare have gained attention as a promising and powerful resource to change the landscape of healthcare. The potential of these technologies for injury prediction, performance analysis, personalized training, and treatment comes with challenges related to the complexity of sports dynamics and the multidimensional aspects of athletic performance.

OBJECTIVES

We aimed to present the current state of AI and ML applications in sports science, specifically in the areas of injury prediction, performance enhancement, and rehabilitation. We also examine the challenges of incorporating AI and ML into sports and suggest directions for future research.

METHOD

We conducted a comprehensive literature review, focusing on publications related to AI and ML applications in sports. This review encompassed studies on injury prediction, performance analysis, and personalized training, emphasizing the AI and ML models applied in sports.

RESULTS

The findings highlight significant advancements in injury prediction accuracy, performance analysis precision, and the customization of training programs through AI and ML. However, future studies need to address challenges such as ethical considerations, data quality, interpretability of ML models, and the integration of complex data.

CONCLUSION

AI and ML may be useful for the prevention, detection, diagnosis, and treatment of health conditions. In this Masterclass paper, we introduce AI and ML concepts, outline recent breakthroughs in AI technologies and their applications, identify the challenges for further progress of AI systems, and discuss ethical issues, clinical and research opportunities, and future perspectives.

Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review

Introduction

In competitive sports, teams are increasingly relying on advanced systems for improved performance and results. This study reviews the literature on the role of artificial intelligence (AI) in managing these complexities and encouraging a system thinking shift. It found various AI applications, including performance enhancement, healthcare, technical and tactical support, talent identification, game prediction, business growth, and AI testing innovations. The main goal of the study was to assess research supporting performance and healthcare.

Methods

Systematic searches were conducted on databases such as Pubmed, Web of Sciences, and Scopus to find articles using AI to understand or improve sports team performance. Thirty-two studies were selected for review.

Results

The analysis shows that, of the thirty-two articles reviewed, fifteen focused on performance and seventeen on healthcare. Football (Soccer) was the most researched sport, making up 67% of studies. The revised studies comprised 2,823 professional athletes, with a gender split of 65.36% male and 34.64% female. Identified AI and non-AI methods mainly included Tree-based techniques (36%), Ada/XGBoost (19%), Neural Networks (9%), K-Nearest Neighbours (9%), Classical Regression Techniques (9%), and Support Vector Machines (6%).

Conclusions

This study highlights the increasing use of AI in managing sports-related healthcare and performance complexities. These findings aim to assist researchers, practitioners, and policymakers in developing practical applications and exploring future complex systems dynamics.

Analyzing ECG signals in professional football players using machine learning techniques

Background

Football player's health is important, and preventing sudden cardiac arrest may be a critical issue. Professional football players have different ECG signals than the average population, yet there are considerable gaps in study whereas the general population has been extensively studied.

Objectives

(a) Generate a reference and innovative resting 12-lead ECG database from 54 UEFA PRO level male football players from La Liga. This is a novel approach to cope the ECG and possible arrythmias in athletes. (b) Manage each XML athlete ECG data and develop a free-use program to visualize, denoise and filter the signal with the capacity to automate the labelling of the waves and save the reports. (c) Study the ECG wave shape and generate models through ML to analyse its utility to automate basic diagnosis.

Methods

The dataset collection is based on a prospective observational cohort and includes 10 s, 12-lead ECGs and rhythm and condition labels for each athlete. Physiological sport arrhythmias, T-Wave shape and other findings were studied and labelled. ECG Visualizer was developed and used for 3 machine learning (ML) methods to automate sinus bradycardia arrhythmia diagnosis.

Results

A dataset with 163 ECGs in XML format was collected comprising the Pro Football 12-lead Resting Electrocardiogram Database (PF12RED). "ECG Visualizer" software was developed, and ML was shown to be useful in detecting sinus bradycardia.

Conclusions

The study demonstrates that AI and machine learning can detect simple arrhythmias with accuracy, also it provides a valuable dataset and a free software application.

Hamstring and knee injuries are associated with isometric hip and trunk muscle strength in elite Australian Rules and Rugby League players

OBJECTIVES

This study investigated relationships between isometric trunk and hip extensor strength, lumbar muscle morphology, and the risk of hamstring and knee ligament injuries in Australian Football League and National Rugby League players.

DESIGN

Prospective cohort study.

METHODS

Trunk and hip extensor strength, multifidus and quadratus lumborum cross-sectional area were measured during the 2020 pre-season. Logistic regressions and decision trees were employed to explore associations between maximum strength, strength endurance, multifidus and quadratus lumborum cross-sectional area, age, previous injuries, and hamstring and knee ligament injury risk.

RESULTS

Greater strength endurance [odds ratio = 0.42 (0.23-0.74), p = 0.004] and maximum strength [odds ratio = 0.55 (0.31-0.94), p = 0.039] reduced hamstring injury risk. Increased risk of knee ligament injuries was associated with larger multifidus [odds ratio = 1.66 (1.14-2.45), p = 0.008] and higher multifidus to quadratus lumborum ratio (odds ratio = 1.57 (1.13-2.23), p = 0.008]. Decision tree models indicated that low strength endurance (< 99 Nm) characterised hamstring strains, while high (≥ 1.33) multifidus to quadratus lumborum ratio mitigated risk. Knee ligament injuries were associated with larger (≥ 8.49 cm2) multifidus, greater (≥ 1.25) multifidus to quadratus lumborum ratio, and lower maximum strength (< 9.24 N/kg).

CONCLUSIONS

Players with lower trunk and hip extensor maximum strength and strength endurance had increased risk of hamstring injuries, while knee ligament injury risk was elevated with larger multifidus cross-sectional area, higher multifidus to quadratus lumborum ratio, and lower maximum trunk and hip extensor strength.

A holistic approach to performance prediction in collegiate athletics: player, team, and conference perspectives

Predictive sports data analytics can be revolutionary for sports performance. Existing literature discusses players' or teams' performance, independently or in tandem. Using Machine Learning (ML), this paper aims to holistically evaluate player-, team-, and conference (season)-level performances in Division-1 Women's basketball. The players were monitored and tested through a full competitive year. The performance was quantified at the player level using the reactive strength index modified (RSImod), at the team level by the game score (GS) metric, and finally at the conference level through Player Efficiency Rating (PER). The data includes parameters from training, subjective stress, sleep, and recovery (WHOOP straps), in-game statistics (Polar monitors), and countermovement jumps. We used data balancing techniques and an Extreme Gradient Boosting (XGB) classifier to predict RSI and GS with greater than 90% accuracy and a 0.9 F1 score. The XGB regressor predicted PER with an MSE of 0.026 and an R2 of 0.680. Ensemble of Random Forest, XGB, and correlation finds feature importance at all levels. We used Partial Dependence Plots to understand the impact of each feature on the target variable. Quantifying and predicting performance at all levels will allow coaches to monitor athlete readiness and help improve training.

Relationship between external and internal load indicators and injury using machine learning in professional soccer: a systematic review and meta-analysis

This study verified the relationship between internal load (IL) and external load (EL) and their association on injury risk (IR) prediction considering machine learning (ML) approaches. Studies were included if: (1) participants were male professional soccer players; (2) carried out for at least 2 sessions, exercises, or competitions; (3) correlated training load (TL) with non-contact injuries; (4) applied ML approaches to predict TL and non-contact injuries. TL included: IL indicators (Rating of Perceived Exertion, RPE; Session-RPE, Heart Rate, HR) and EL indicators (Global Positioning System, GPS variables); the relationship between EL and IL through index, ratio, formula; ML indicators included performance measures, predictive performance of ML methods, measure of feature importance, relevant predictors, outcome variable, predictor variable, data pre-processing, features selection, ML methods. Twenty-five studies were included. Eleven addressed the relationship between EL and IL. Five used EL/IL indexes. Five studies predicted IL indicators. Three studies investigated the association between EL and IL with IR. One study predicted IR using ML. Significant positive correlations were found between S-RPE and total distance (TD) (r = 0.73; 95% CI (0.64 to 0.82)) as well as between S-RPE and player load (PL) (r = 0.76; 95% CI (0.68 to 0.84)). Association between IL and EL and their relationship with injuries were found. RPE, S-RPE, and HR were associated with different EL indicators. A positive relationship between EL and IL indicators and IR was also observed. Moreover, new indexes or ratios (integrating EL and IL) to improve knowledge regarding TL and fitness status were also applied. ML can predict IL indicators (HR and RPE), and IR. The present systematic review was registered in PROSPERO (CRD42021245312).

Managing Lower Limb Muscle Reinjuries in Athletes: From Risk Factors to Return-to-Play Strategies

Muscle injuries and subsequent reinjuries significantly impact athletes, especially in football. These injuries lead to time loss, performance impairment, and long-term health concerns. This review aims to provide a comprehensive overview of the current understanding of muscle reinjuries, delving into their epidemiology, risk factors, clinical management, and prevention strategies. Despite advancements in rehabilitation programs and return-to-play criteria, reinjury rates remain alarmingly high. Age and previous muscle injuries are nonmodifiable risk factors contributing to a high reinjury rate. Clinical management, which involves accurate diagnosis, individualized rehabilitation plans, and the establishment of return-to-training and return-to-play criteria, plays a pivotal role during the sports season. Eccentric exercises, optimal loading, and training load monitoring are key elements in preventing reinjuries. The potential of artificial intelligence (AI) in predicting and preventing reinjuries offers a promising avenue, emphasizing the need for a multidisciplinary approach to managing these injuries. While current strategies offer some mitigation, there is a pressing need for innovative solutions, possibly leveraging AI, to reduce the incidence of muscle reinjuries in football players. Future research should focus on this direction, aiming to enhance athletes' well-being and performance.

Blood-Based Biomarkers for Managing Workload in Athletes: Perspectives for Research on Emerging Biomarkers

At present, various blood-based biomarkers have found their applications in the field of sports medicine. This current opinion addresses biomarkers that warrant consideration in future research for monitoring the athlete training load. In this regard, we identified a variety of emerging load-sensitive biomarkers, e.g., cytokines (such as IL-6), chaperones (such as heat shock proteins) or enzymes (such as myeloperoxidase) that could improve future athlete load monitoring as they have shown meaningful increases in acute and chronic exercise settings. In some cases, they have even been linked to training status or performance characteristics. However, many of these markers have not been extensively studied and the cost and effort of measuring these parameters are still high, making them inconvenient for practitioners so far. We therefore outline strategies to improve knowledge of acute and chronic biomarker responses, including ideas for standardized study settings. In addition, we emphasize the need for methodological advances such as the development of minimally invasive point-of-care devices as well as statistical aspects related to the evaluation of these monitoring tools to make biomarkers suitable for regular load monitoring.

Strengths, weaknesses, opportunities, and threats associated with the application of artificial intelligence in connection with sport research, coaching, and optimization of athletic performance: a brief SWOT analysis

Here, we performed a non-systematic analysis of the strength, weaknesses, opportunities, and threats (SWOT) associated with the application of artificial intelligence to sports research, coaching and optimization of athletic performance. The strength of AI with regards to applied sports research, coaching and athletic performance involve the automation of time-consuming tasks, processing and analysis of large amounts of data, and recognition of complex patterns and relationships. However, it is also essential to be aware of the weaknesses associated with the integration of AI into this field. For instance, it is imperative that the data employed to train the AI system be both diverse and complete, in addition to as unbiased as possible with respect to factors such as the gender, level of performance, and experience of an athlete. Other challenges include e.g., limited adaptability to novel situations and the cost and other resources required. Opportunities include the possibility to monitor athletes both long-term and in real-time, the potential discovery of novel indicators of performance, and prediction of risk for future injury. Leveraging these opportunities can transform athletic development and the practice of sports science in general. Threats include over-dependence on technology, less involvement of human expertise, risks with respect to data privacy, breaching of the integrity and manipulation of data, and resistance to adopting such new technology. Understanding and addressing these SWOT factors is essential for maximizing the benefits of AI while mitigating its risks, thereby paving the way for its successful integration into sport science research, coaching, and optimization of athletic performance.

An Overview of Machine Learning Applications in Sports Injury Prediction

Use injuries, i.e., injuries caused by repetitive strain on the body, represent a serious problem in athletics that has traditionally relied on historic datasets and human experience for prevention. Existing methodologies have been frustratingly slow at developing higher precision prevention practices. Technological advancements have permitted the emergence of artificial intelligence and machine learning (ML) as promising toolsets to enhance both injury mitigation and rehabilitation protocols. This article provides a comprehensive overview of recent advances in ML techniques as they have been applied to sports injury prediction and prevention. A comprehensive literature review was conducted searching PubMed/Medline, Institute of Electrical and Electronics Engineers (IEEE)/Institute of Engineering and Technology (IET), and ScienceDirect. Ovid Discovery and Google Scholar were used to provide additional aggregate results and a grey literature search. A focus was placed on papers published from 2017 to 2022. Algorithms of interest were limited to K-Nearest Neighbor (KNN), K-means, decision tree, random forest, gradient boosting and AdaBoost, and neural networks. A total of 42 original research papers were included, and their results were summarized. We conclude that given the current lack of open source, uniform data sets, as well as a reliance on dated regression models, no strong conclusions about the real-world efficacy of ML as it applies to sports injury prediction can be made. However, it is suggested that addressing these two issues will allow powerful, novel ML architectures to be deployed, thus rapidly advancing the state of this field, and providing validated clinical tools.

Introduction and Comparison of Novel Decentral Learning Schemes with Multiple Data Pools for Privacy-Preserving ECG Classification

Artificial intelligence and machine learning have led to prominent and spectacular innovations in various scenarios. Application in medicine, however, can be challenging due to privacy concerns and strict legal regulations. Methods that centralize knowledge instead of data could address this issue. In this work, 6 different decentralized machine learning algorithms are applied to 12-lead ECG classification and compared to conventional, centralized machine learning. The results show that state-of-the-art federated learning leads to reasonable losses of classification performance compared to a standard, central model (-0.054 AUROC) while providing a significantly higher level of privacy. A proposed weighted variant of federated learning (-0.049 AUROC) and an ensemble (-0.035 AUROC) outperformed the standard federated learning algorithm. Overall, considering multiple metrics, the novel batch-wise sequential learning scheme performed best (-0.036 AUROC to baseline). Although, the technical aspects of implementing them in a real-world application are to be carefully considered, the described algorithms constitute a way forward towards preserving-preserving AI in medicine.

Externally validated machine learning algorithm accurately predicts medial tibial stress syndrome in military trainees: a multicohort study

Objectives

Medial tibial stress syndrome (MTSS) is a common musculoskeletal injury in both sporting and military settings. No reliable treatments exist, and reoccurrence rates are high. Prevention of MTSS is critical to reducing operational burden. Therefore, this study aimed to build a decision-making model to predict the individual risk of MTSS within officer cadets and test the external validity of the model on a separate military population.

Design

Prospective cohort study.

Methods

This study collected a suite of key variables previously established for predicting MTSS. Data were obtained from 107 cadets (34 women and 73 men). A follow-up survey was conducted at 3 months to determine MTSS diagnoses. Six ensemble learning algorithms were deployed and trained five times on random stratified samples of 75% of the dataset. The resultant algorithms were tested on the remaining 25% of the dataset, with models then compared for accuracy. The most accurate new algorithm was tested on an unrelated data sample of 123 Australian Navy recruits to establish external validity of the model.

Results

Calibrated random forest modelling was the most accurate in identifying a diagnosis of MTSS; (area under curve (AUC)=98%, classification accuracy (CA)=96%). External validation on a sample of Navy recruits resulted in comparable accuracy; (AUC=95%, CA=94%). When the model was tested on the combined datasets, similar accuracy was achieved; (AUC=92%, CA=91%).

Conclusion

This model is highly accurate in predicting those who will develop MTSS. The model provides important preventive capacity which should be trialled as a risk management intervention.

Musculoskeletal Injury Risk Stratification: A Traffic Light System for Military Service Members

Risk factor identification is a critical first step in informing musculoskeletal injury (MSKI) risk mitigation strategies. This investigation aimed to determine if a self-reported MSKI risk assessment can accurately identify military service members at greater MSKI risk and determine whether a traffic light model can differentiate service members' MSKI risks. A retrospective cohort study was conducted using existing self-reported MSKI risk assessment data and MSKI data from the Military Health System. A total of 2520 military service members (2219 males: age 23.49 ± 5.17 y, BMI 25.11 ± 2.94 kg/m²; and 301 females: age 24.23 ± 5.85 y, BMI 25.59 ± 3.20 kg/m², respectively) completed the MSKI risk assessment during in-processing. The risk assessment consisted of 16 self-report items regarding demographics, general health, physical fitness, and pain experienced during movement screens. These 16 data points were converted to 11 variables of interest. For each variable, service members were dichotomized as at risk or not at risk. Nine of the 11 variables were associated with a greater MSKI risk and were thus considered as risk factors for the traffic light model. Each traffic light model included three color codes (i.e., green, amber, and red) to designate risk (i.e., low, moderate, and high). Four traffic light models were generated to examine the risk and overall precision of different cut-off values for the amber and red categories. In all four models, service members categorized as amber [hazard ratio (HR) = 1.38-1.70] or red (HR = 2.67-5.82) were at a greater MSKI risk. The traffic light model may help prioritize service members who require individualized orthopedic care and MSKI risk mitigation plans.

Cutting-Edge Research in Sports Biomechanics: From Basic Science to Applied Technology

Sports biomechanics is the study of the mechanical principles of human movement and how they apply to sports performance [...].

Predictive modeling of the ratings of perceived exertion during training and competition in professional soccer players

OBJECTIVES

Evaluate the ability of predicting the ratings of perceived exertion from the external load variables in professional soccer players through a chronological perspective (i.e., past features values are considered additional features) through machine learning models by considering the playing position.

DESIGN

Prospective cohort study.

METHODS

Thirty-eight elite soccer players aged 19-27 years were observed during 151 training sessions, 44 matches across a full season. External load variables (58 derived from Global Positioning System and 30 from accelerometers) and the internal load derived from ratings of perceived exertion were collected for each player and each session and match. Machine learning models (linear regression, K-NN, decision trees, random forest, elastic net regression, XGBoost) were compared and interpreted in order to deepen the relationship between external load variables and ratings of perceived exertion according to the player position in a predictive perspective.

RESULTS

Application of the machine learning models on the dataset provided enough predictive power to reduce the Root Mean Squared Error of 60 % from dummy predictions. The most accurate models (Root Mean Squared Error ≈ 1.1 for random forest and = 1 for XGBoost) highlighted a memory effect in subsequent ratings of perceived exertion values. Past ratings of perceived exertion values over one month were the strongest predicting factors of ratings of perceived exertion as compared to various external load indicators.

CONCLUSIONS

The tree-based machine learning models showed statistically significant predictive ability, indicating valuable information for understanding the training load responses based on ratings of perceived exertion changes.

Microbiological Risk Assessment in Foods: Background and Tools, with a Focus on Risk Ranger

Risk assessment is an important phase of the food production path; it is strictly related to the processing chain as a necessary step for safe foods. This paper represents a contribution to understanding what is and how risk assessment could be conducted; it aims to provide some information on the structure of risk assessment, the tools for its identification and measurement and the importance of risk assessment for correct communication. In this context, after a focus on the background and on some commonly used tools (Risk Ranger, FDA-iRisk, decision tree, among others), the paper describes how to perform risk assessment through three case studies: lettuce (for Listeria monocytogenes), chicken salad (for Escherichia coli), and fresh egg pasta (for Staphylococcus aureus) in the first step, and then a comparison of risk for chicken salad contaminated by different pathogens (E. coli O157:H7, Campylobacter spp. and Salmonella sp.). As a final step, a critical evaluation of Risk Ranger was carried out, pointing out its pros and cons.

Predicting Severity of Head Collision Events in Elite Soccer Using Preinjury Data: A Machine Learning Approach

OBJECTIVE

To develop machine learning (ML) models that predict severity of head collision events (HCEs) based on preinjury variables and to investigate which variables are important to predicting severity.

DESIGN

Data on HCEs were collected with respect to severity and 23 preinjury variables to create 2 datasets, a male dataset using men's tournaments and mixed dataset using men's and women's tournaments, to perform ML analysis. Machine learning analysis used a random forest classifier based on preinjury variables to predict HCE severity.

SETTING

Four elite international soccer tournaments.

PARTICIPANTS

Elite athletes participating in analyzed tournaments.

INDEPENDENT VARIABLES

The 23 preinjury variables collected for each HCE.

MAIN OUTCOME MEASURES

Predictive ability of the ML models and association of important variables.

RESULTS

The ML models had an average area under the receiver operating characteristic curve for predicting HCE severity of 0.73 and 0.70 for the male and mixed datasets, respectively. The most important variables for prediction were the mechanism of injury and the event before injury. In the male dataset, the mechanisms "head-to-head" and "knee-to-head" were together significantly associated ( P = 0.0244) with severity; they were not significant in the mixed dataset ( P = 0.1113). In both datasets, the events "corner kicks" and "throw-ins" were together significantly associated with severity (male, P = 0.0001; mixed, P = 0.0004).

CONCLUSIONS

ML models accurately predicted the severity of HCE. The mechanism and event preceding injury were most important for predicting severity of HCEs. These findings support the use of ML to inform preventative measures that will mitigate the impact of these preinjury factors on player health.

Prediction of Marathon Performance using Artificial Intelligence

Although studies used machine learning algorithms to predict performances in sports activities, none, to the best of our knowledge, have used and validated two artificial intelligence techniques: artificial neural network (ANN) and k-nearest neighbor (KNN) in the running discipline of marathon and compared the accuracy or precision of the predicted performances. Official French rankings for the 10-km road and marathon events in 2019 were scrutinized over a dataset of 820 athletes (aged 21, having run 10 km and a marathon in the same year that was run slower, etc.). For the KNN and ANN the same inputs (10-km race time, body mass index, age and sex) were used to solve a linear regression problem to estimate the marathon race time. No difference was found between the actual and predicted marathon performances for either method (p>0,05). All predicted performances were significantly correlated with the actual ones, with very high correlation coefficients (r>0,90; p<0,001). KNN outperformed ANN with a mean absolute error of 2,4 vs 5,6%. The study confirms the validity of both algorithms, with better accuracy for KNN in predicting marathon performance. Consequently, the predictions from these artificial intelligence methods may be used in training programs and competitions.

Machine learning aided jump height estimate democratization through smartphone measures

Introduction

The peak height reached in a countermovement jump is a well established performance parameter. Its estimate is often entrusted to force platforms or body-worn inertial sensors. To date, smartphones may possibly be used as an alternative for estimating jump height, since they natively embed inertial sensors.

Methods

For this purpose, 43 participants performed 4 countermovement jumps (172 in total) on two force platforms (gold standard). While jumping, participants held a smartphone in their hands, whose inertial sensor measures were recorded. After peak height was computed for both instrumentations, twenty-nine features were extracted, related to jump biomechanics and to signal time-frequency characteristics, as potential descriptors of soft tissues or involuntary arm swing artifacts. A training set (129 jumps - 75%) was created by randomly selecting elements from the initial dataset, the remaining ones being assigned to the test set (43 jumps - 25%). On the training set only, a Lasso regularization was applied to reduce the number of features, avoiding possible multicollinearity. A multi-layer perceptron with one hidden layer was trained for estimating the jump height from the reduced feature set. Hyperparameters optimization was performed on the multi-layer perceptron using a grid search approach with 5-fold cross validation. The best model was chosen according to the minimum negative mean absolute error.

Results

The multi-layer perceptron greatly improved the accuracy (4 cm) and precision (4 cm) of the estimates on the test set with respect to the raw smartphone measures estimates (18 and 16 cm, respectively). Permutation feature importance was performed on the trained model in order to establish the influence that each feature had on the outcome. The peak acceleration and the braking phase duration resulted the most influential features in the final model. Despite not being accurate enough, the height computed through raw smartphone measures was still among the most influential features.

Discussion

The study, implementing a smartphone-based method for jump height estimates, paves the way to method release to a broader audience, pursuing a democratization attempt.

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.

Machine learning application in soccer: a systematic review

Due to the chaotic nature of soccer, the predictive statistical models have become in a current challenge to decision-making based on scientific evidence. The aim of the present study was to systematically identify original studies that applied machine learning (ML) to soccer data, highlighting current possibilities in ML and future applications. A systematic review of PubMed, SPORTDiscus, and FECYT (Web of Sciences, CCC, DIIDW, KJD, MEDLINE, RSCI, and SCIELO) was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 145 studies initially identified, 32 were fully reviewed, and their outcome measures were extracted and analyzed. In summary, all articles were clustered into three groups: injury (n = 7); performance (n = 21), which was classified in match/league outcomes forecasting, physical/physiological forecasting, and technical/tactical forecasting; and the last group was about talent forecasting (n = 5). The development of technology, and subsequently the large amount of data available, has become ML in an important strategy to help team staff members in decision-making predicting dose-response relationship reducing the chaotic nature of this team sport. However, since ML models depend upon the amount of dataset, further studies should analyze the amount of data input needed make to a relevant predictive attempt which makes accurate predicting available.

A review of machine learning applications in soccer with an emphasis on injury risk

This narrative review paper aimed to discuss the literature on machine learning applications in soccer with an emphasis on injury risk assessment. A secondary aim was to provide practical tips for the health and performance staff in soccer clubs on how machine learning can provide a competitive advantage. Performance analysis is the area with the majority of research so far. Other domains of soccer science and medicine with machine learning use are injury risk assessment, players' workload and wellness monitoring, movement analysis, players' career trajectory, club performance, and match attendance. Regarding injuries, which is a hot topic, machine learning does not seem to have a high predictive ability at the moment (models specificity ranged from 74.2%-97.7%. sensitivity from 15.2%-55.6% with area under the curve of 0.66-0.83). It seems, though, that machine learning can help to identify the early signs of elevated risk for a musculoskeletal injury. Future research should account for musculoskeletal injuries' dynamic nature for machine learning to provide more meaningful results for practitioners in soccer.

Conceptual Structure and Current Trends in Artificial Intelligence, Machine Learning, and Deep Learning Research in Sports: A Bibliometric Review

Artificial intelligence and its subcategories of machine learning and deep learning are gaining increasing importance and attention in the context of sports research. This has also meant that the number of corresponding publications has become complex and unmanageably large in human terms. In the current state of the research field, there is a lack of bibliometric analysis, which would prove useful for obtaining insights into the large amounts of available literature. Therefore, the present work aims to identify important research issues, elucidate the conceptual structure of the research field, and unpack the evolutionary trends and the direction of hot topics regarding key themes in the research field of artificial intelligence in sports. Using the Scopus database, 1215 documents (reviews and articles) were selected. Bibliometric analysis was performed using VOSviewer and bibliometrix R package. The main findings are as follows: (a) the literature and research interest concerning AI and its subcategories is growing exponentially; (b) the top 20 most cited works comprise 32.52% of the total citations; (c) the top 10 journals are responsible for 28.64% of all published documents; (d) strong collaborative relationships are present, along with small, isolated collaboration networks of individual institutions; (e) the three most productive countries are China, the USA, and Germany; (f) different research themes can be characterized using author keywords with current trend topics, e.g., in the fields of biomechanics, injury prevention or prediction, new algorithms, and learning approaches. AI research activities in the fields of sports pedagogy, sports sociology, and sports economics seem to have played a subordinate role thus far. Overall, the findings of this study expand knowledge on the research situation as well as the development of research topics regarding the use of artificial intelligence in sports, and may guide researchers to identify currently relevant topics and gaps in the research.

Cloud-based deep learning-assisted system for diagnosis of sports injuries

At both clinical and diagnostic levels, machine learning technologies could help facilitate medical decision-making. Prediction of sports injuries, for instance, is a key component of avoiding and minimizing injury in motion. Despite significant attempts to forecast sports injuries, the present method is limited by its inability to identify predictors. When designing measures for the avoidance of work-related accidents and the reduction of associated risks, the risk of injury to athletes is a crucial consideration. Various indicators are being evaluated to identify injury risk factors in a number of different methods. Consequently, this paper proposes a Deep Learning-assisted System (DLS) for diagnosing sports injuries using the Internet of Things (IoT) and the concept of cloud computing. The IoT sensors that compose the body area network collect crucial data for the diagnosis of sports injuries, while cloud computing makes available flexible computer system resources and computing power. This research examines the brain injury monitoring framework, uses an optimal neural network to forecast brain injury, and enhances the medical rehabilitation system for sports. Using the metrics accuracy, precision, recall, and F1-score, the performance of the proposed model is assessed and compared with current models.

How to Increase Sport Facility Users' Intention to Use AI Fitness Services: Based on the Technology Adoption Model

Artificial intelligence (AI) has recently been introduced as a new way of analyzing and predicting sport consumer behavior. The goal of this study was to investigate the relationships among the perceived usefulness, perceived ease of use, the importance of exercise, attitudes towards use, and the behavioral intention to use AI services based on the technology adoption model. The authors recruited 408 participants who participated in an experiment designed to provide a deeper understanding of AI fitness services. After screening, the collected data were screened through assumption tests, and we conducted a confirmatory factor analysis and structural equation modeling to analyze research hypotheses. The results indicated that three types of consumer evaluations (i.e., perceived usefulness, perceived ease of use, and importance of exercise) positively influence their attitudes toward AI fitness services. In addition, the positive attitudes regarding AI services positively influenced the intention to use AI services. The results of this research contribute to our knowledge of the consumers' attitudes and behaviors toward AI services in the sport industry based on the technology acceptance model. Furthermore, this study provided the empirical evidence critically needed to increase our understanding of AI in the sport industry and offered new insights into how sport facility managers can predict their consumers' intention to use AI services.

xLength: Predicting Expected Ski Jump Length Shortly after Take-Off Using Deep Learning

With tracking systems becoming more widespread in sports research and regular training and competitions, more data are available for sports analytics and performance prediction. We analyzed 2523 ski jumps from 205 athletes on five venues. For every jump, the dataset includes the 3D trajectory, 3D velocity, skis' orientation, and metadata such as wind, starting gate, and ski jumping hill data. Using this dataset, we aimed to predict the expected jump length (xLength) inspired by the expected goals metric in soccer (xG). We evaluate the performance of a fully connected neural network, a convolutional neural network (CNN), a long short-term memory (LSTM), and a ResNet architecture to estimate the xLength. For the prediction of the jump length one second after take-off, we achieve a mean absolute error (MAE) of 5.3 m for the generalization to new athletes and an MAE of 5.9 m for the generalization to new ski jumping hills using ResNet architectures. Additionally, we investigated the influence of the input time after the take-off on the predictions' accuracy. As expected, the MAE becomes smaller with longer inputs. Due to the real-time transmission of the sensor's data, xLength can be updated during the flight phase and used in live TV broadcasting. xLength could also be used as an analysis tool for experts to quantify the quality of the take-off and flight phases.

A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks

Lifting is one of the most potentially harmful activities for work-related musculoskeletal disorders (WMSDs), due to exposure to biomechanical risk. Risk assessment for work activities that involve lifting loads can be performed through the NIOSH (National Institute of Occupational Safety and Health) method, and specifically the Revised NIOSH Lifting Equation (RNLE). Aim of this work is to explore the feasibility of a logistic regression model fed with time and frequency domains features extracted from signals acquired through one inertial measurement unit (IMU) to classify risk classes associated with lifting activities according to the RNLE. Furthermore, an attempt was made to evaluate which are the most discriminating features relating to the risk classes, and to understand which inertial signals and which axis were the most representative. In a simplified scenario, where only two RNLE variables were altered during lifting tasks performed by 14 healthy adults, inertial signals (linear acceleration and angular velocity) acquired using one IMU placed on the subject’s sternum during repeated rhythmic lifting tasks were automatically segmented to extract several features in the time and frequency domains. The logistic regression model fed with significant features showed good results to discriminate “risk” and “no risk” NIOSH classes with an accuracy, sensitivity and specificity equal to 82.8%, 84.8% and 80.9%, respectively. This preliminary work indicated that a logistic regression model—fed with specific inertial features extracted by signals acquired using a single IMU sensor placed on the sternum—is able to discriminate risk classes according to the RNLE in a simplified context, and therefore could be a valid tool to assess the biomechanical risk in an automatic way also in more complex conditions (e.g., real working scenarios).

Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics

In this study, we investigated the relationships between training load, perceived wellness and match performance in professional volleyball by applying the machine learning techniques XGBoost, random forest regression and subgroup discovery. Physical load data were obtained by manually logging all physical activities and using wearable sensors. Daily wellness of players was monitored using questionnaires. Match performance was derived from annotated actions by a video scout during matches. We identified conditions of predictor variables that related to attack and pass performance (p < 0.05). Better attack performance is related to heavy weights of lower-body strength training exercises in the preceding four weeks. However, worse attack performance is linked to large variations in weights of full-body strength training exercises, excessively heavy upper-body strength training, low jump heights and small variations in the number of high jumps in the four weeks prior to competition. Lower passing performance was associated with small variations in the number of high jumps in the preceding week and an excessive amount of high jumps performed, on average, in the two weeks prior to competition. Differences in findings with respect to passing and attack performance suggest that elite volleyball players can improve their performance if training schedules are adapted to the position of a player.

Wearable Sensor Technology to Predict Core Body Temperature: A Systematic Review

Heat-related illnesses, which range from heat exhaustion to heatstroke, affect thousands of individuals worldwide every year and are characterized by extreme hyperthermia with the core body temperature (CBT) usually > 40 °C, decline in physical and athletic performance, CNS dysfunction, and, eventually, multiorgan failure. The measurement of CBT has been shown to predict heat-related illness and its severity, but the current measurement methods are not practical for use in high acuity and high motion settings due to their invasive and obstructive nature or excessive costs. Noninvasive predictions of CBT using wearable technology and predictive algorithms offer the potential for continuous CBT monitoring and early intervention to prevent HRI in athletic, military, and intense work environments. Thus far, there has been a lack of peer-reviewed literature assessing the efficacy of wearable devices and predictive analytics to predict CBT to mitigate heat-related illness. This systematic review identified 20 studies representing a total of 25 distinct algorithms to predict the core body temperature using wearable technology. While a high accuracy in prediction was noted, with 17 out of 18 algorithms meeting the clinical validity standards. few algorithms incorporated individual and environmental data into their core body temperature prediction algorithms, despite the known impact of individual health and situational and environmental factors on CBT. Robust machine learning methods offer the ability to develop more accurate, reliable, and personalized CBT prediction algorithms using wearable devices by including additional data on user characteristics, workout intensity, and the surrounding environment. The integration and interoperability of CBT prediction algorithms with existing heat-related illness prevention and treatment tools, including heat indices such as the WBGT, athlete management systems, and electronic medical records, will further prevent HRI and increase the availability and speed of data access during critical heat events, improving the clinical decision-making process for athletic trainers and physicians, sports scientists, employers, and military officers.

Daily and weekly external loads in the microcycle: Characterization and comparison between playing positions on amateur soccer

Ensuring adequate levels of training and recovery to maximize player performance is critical; however, there are methodological challenges in designing a periodized training program for soccer teams. This study aims to describe and characterize the daily and weekly external load in an amateur soccer team and based on the weighting factors determined by the match reference, compare the external loads between playing positions. Twenty-four amateur soccer players (22.3 ± 1.7 years) were monitored using a global positioning system. Data collected comprises 19 competitive microcycles with a standard structure composed of 3 training sessions (matchday-5, matchday-3, and matchday-2) and one match. Match-reference values were calculated as the mean of the five best values recorded during official matches. The results show, on matchday-5 session, the existence of significant differences between playing positions to relative total distance covered (p = 0.050), relative sprint distance (p = 0.001), relative moderate-intensity accelerations (p < 0.001), relative high-intensity accelerations (p = 0.003), relative moderate-intensity decelerations (p < 0.001), and relative high-intensity decelerations (p = 0.017). On matchday-3 session, there are significant differences to relative very high-speed running distance (p = 0.017) and relative moderate-intensity decelerations (p = 0.014). On matchday-2 session, there are significant differences to relative high-speed running distance (p = 0.025), relative very high-speed running distance (p = 0.008), and relative moderate-intensity decelerations (p < 0.001). Weekly significant differences are observed between the playing positions to relative moderate-intensity accelerations (p = 0.002), relative high-intensity accelerations (p < 0.001), and relative moderate-intensity decelerations (p < 0.001). The weekly load is characterized by a greater weighting on accelerations and decelerations, compared to distances at very-high speed and sprint. The training loads must respect a standard training model that contemplates the individualization of the physical demands of the match, for each playing position, as for each individual.

A novel lower extremity non-contact injury risk prediction model based on multimodal fusion and interpretable machine learning

The application of machine learning algorithms in studying injury assessment methods based on data analysis has recently provided a new research insight for sports injury prevention. However, the data used in these studies are primarily multi-source and multimodal (i.e., longitudinal repeated-measures data and cross-sectional data), resulting in the models not fully utilising the information in the data to reveal specific injury risk patterns. Therefore, this study proposed an injury risk prediction model based on a multi-modal strategy and machine learning algorithms to handle multi-source data better and predict injury risk. This study retrospectively analysed the routine monitoring data of sixteen young female basketball players. These data included training load, perceived well-being status, physiological response, physical performance and lower extremity non-contact injury registration. This study partitions the original dataset based on the frequency of data collection. Extreme gradient boosting (XGBoost) was used to construct unimodal submodels to obtain decision scores for each category of indicators. Ultimately, the decision scores from each submodel were fused using the random forest (RF) to generate a lower extremity non-contact injury risk prediction model at the decision-level. The 10-fold cross-validation results showed that the fusion model was effective in classifying non-injured (mean Precision: 0.9932, mean Recall: 0.9976, mean F2-score: 0.9967), minimal lower extremity non-contact injuries risk (mean Precision: 0.9317, mean Recall: 0.9167, mean F2-score: 0.9171), and mild lower extremity non-contact injuries risk (mean Precision: 0.9000, mean Recall: 0.9000, mean F2-score: 0.9000). The model performed significantly more optimal than the submodel. Comparing the fusion model proposed with a traditional data integration scheme, the average Precision and Recall improved by 8.2 and 20.3%, respectively. The decision curves analysis showed that the proposed fusion model provided a higher net benefit to athletes with potential lower extremity non-contact injury risk. The validity, feasibility and practicality of the proposed model have been confirmed. In addition, the shapley additive explanation (SHAP) and network visualisation revealed differences in lower extremity non-contact injury risk patterns across severity levels. The model proposed in this study provided a fresh perspective on injury prevention in future research.

Using Artificial Intelligence-Enhanced Sensing and Wearable Technology in Sports Medicine and Performance Optimisation

Wearable technologies are small electronic and mobile devices with wireless communication capabilities that can be worn on the body as a part of devices, accessories or clothes. Sensors incorporated within wearable devices enable the collection of a broad spectrum of data that can be processed and analysed by artificial intelligence (AI) systems. In this narrative review, we performed a literature search of the MEDLINE, Embase and Scopus databases. We included any original studies that used sensors to collect data for a sporting event and subsequently used an AI-based system to process the data with diagnostic, treatment or monitoring intents. The included studies show the use of AI in various sports including basketball, baseball and motor racing to improve athletic performance. We classified the studies according to the stage of an event, including pre-event training to guide performance and predict the possibility of injuries; during events to optimise performance and inform strategies; and in diagnosing injuries after an event. Based on the included studies, AI techniques to process data from sensors can detect patterns in physiological variables as well as positional and kinematic data to inform how athletes can improve their performance. Although AI has promising applications in sports medicine, there are several challenges that can hinder their adoption. We have also identified avenues for future work that can provide solutions to overcome these challenges.

Using global navigation satellite systems for modeling athletic performances in elite football players

This study aims to predict individual Acceleration-Velocity profiles (A-V) from Global Navigation Satellite System (GNSS) measurements in real-world situations. Data were collected from professional players in the Superleague division during a 1.5 season period (2019–2021). A baseline modeling performance was provided by time-series forecasting methods and compared with two multivariate modeling approaches using ridge regularisation and long short term memory neural networks. The multivariate models considered commercial features and new features extracted from GNSS raw data as predictor variables. A control condition in which profiles were predicted from predictors of the same session outlined the predictability of A-V profiles. Multivariate models were fitted either per player or over the group of players. Predictor variables were pooled according to the mean or an exponential weighting function. As expected, the control condition provided lower error rates than other models on average (p = 0.001). Reference and multivariate models did not show significant differences in error rates (p = 0.124), regardless of the nature of predictors (commercial features or extracted from signal processing methods) or the pooling method used. In addition, models built over a larger population did not provide significantly more accurate predictions. In conclusion, GNSS features seemed to be of limited relevance for predicting individual A-V profiles. However, new signal processing features open up new perspectives in athletic performance or injury occurrence modeling, mainly if higher sampling rate tracking systems are considered.

AI provides congruent and prescriptive feedback for squat form: qualitative assessment of coaching provided by AI and physical therapist

Objectives: To assess style and themes of feedback provided by artificial intelligence (AI) mobile application and physical therapist (PT) to participants during bodyweight squat exercise. Methods: Research population was age 20-35, without any pre-existing condition that precluded participation in bodyweight exercise. Qualitative methodology followed directed content analysis. Cohen's kappa coefficient verified consistency between coders. Results: Both AI and PT groups had seven female and eight male participants. Three themes emerged: affirmation schema, correction paradigms and physical assessments. Average kappa coefficient calculated for all codes was 0.96, a value that indicates almost perfect agreement. Conclusion: Themes generated highlight the AI focus on congruent, descriptive and prescriptive feedback, while the PT demonstrated multipoint improvement capabilities. Further research should establish feedback comparisons with multiple PTs and correlate qualitative data with additional quantitative data on performance outcomes based on feedback.

Artificial Intelligence in Elite Sports—A Narrative Review of Success Stories and Challenges

This paper explores the role of artificial intelligence (AI) in elite sports. We approach the topic from two perspectives. Firstly, we provide a literature based overview of AI success stories in areas other than sports. We identified multiple approaches in the area of Machine Perception, Machine Learning and Modeling, Planning and Optimization as well as Interaction and Intervention, holding a potential for improving training and competition. Secondly, we discover the present status of AI use in elite sports. Therefore, in addition to another literature review, we interviewed leading sports scientist, which are closely connected to the main national service institute for elite sports in their countries. The analysis of this literature review and the interviews show that the most activity is carried out in the methodical categories of signal and image processing. However, projects in the field of modeling & planning have become increasingly popular within the last years. Based on these two perspectives, we extract deficits, issues and opportunities and summarize them in six key challenges faced by the sports analytics community. These challenges include data collection, controllability of an AI by the practitioners and explainability of AI results.

Identifying the Current State and Improvement Opportunities in the Information Flows Necessary to Manage Professional Athletes: A Case Study in Rugby Union

In sporting environments, the knowledge necessary to manage athletes is built on information flows associated with player management processes. In current literature, there are limited case studies available to illustrate how such information flows are optimized. Hence, as the first step of an optimization project, this study aimed to evaluate the current state and the improvement opportunities in the player management information flow executed within the High-Performance Unit (HPU) at a professional rugby union club in England. Guided by a Business Process Management framework, elicitation of the current process architecture illustrated the existence of 18 process units and two core process value chains relating to player management. From the identified processes, the HPU management team prioritized 7 processes for optimization. In-depth details on the current state (As-Is) of the selected processes were extracted from semi-structured, interview-based process discovery and were modeled using Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) standards. Results were presented for current issues in the information flow of the daily training load management process, identified through a thematic analysis conducted on the data obtained mainly from focus group discussions with the main stakeholders (physiotherapists, strength and conditioning coaches, and HPU management team) of the process. Specifically, the current state player management information flow in the HPU had issues relating to knowledge creation and process flexibility. Therefore, the results illustrate that requirements for information flow optimization within the considered environment exist in the transition from data to knowledge during the execution of player management decision-making processes.

Just How Confident Can We Be in Predicting Sports Injuries? A Systematic Review of the Methodological Conduct and Performance of Existing Musculoskeletal Injury Prediction Models in Sport

BACKGROUND

An increasing number of musculoskeletal injury prediction models are being developed and implemented in sports medicine. Prediction model quality needs to be evaluated so clinicians can be informed of their potential usefulness.

OBJECTIVE

To evaluate the methodological conduct and completeness of reporting of musculoskeletal injury prediction models in sport.

METHODS

A systematic review was performed from inception to June 2021. Studies were included if they: (1) predicted sport injury; (2) used regression, machine learning, or deep learning models; (3) were written in English; (4) were peer reviewed.

RESULTS

Thirty studies (204 models) were included; 60% of studies utilized only regression methods, 13% only machine learning, and 27% both regression and machine learning approaches. All studies developed a prediction model and no studies externally validated a prediction model. Two percent of models (7% of studies) were low risk of bias and 98% of models (93% of studies) were high or unclear risk of bias. Three studies (10%) performed an a priori sample size calculation; 14 (47%) performed internal validation. Nineteen studies (63%) reported discrimination and two (7%) reported calibration. Four studies (13%) reported model equations for statistical predictions and no machine learning studies reported code or hyperparameters.

CONCLUSION

Existing sport musculoskeletal injury prediction models were poorly developed and have a high risk of bias. No models could be recommended for use in practice. The majority of models were developed with small sample sizes, had inadequate assessment of model performance, and were poorly reported. To create clinically useful sports musculoskeletal injury prediction models, considerable improvements in methodology and reporting are urgently required.

Machine Learning for Understanding and Predicting Injuries in Football

Attempts to better understand the relationship between training and competition load and injury in football are essential for helping to understand adaptation to training programmes, assessing fatigue and recovery, and minimising the risk of injury and illness. To this end, technological advancements have enabled the collection of multiple points of data for use in analysis and injury prediction. The full breadth of available data has, however, only recently begun to be explored using suitable statistical methods. Advances in automatic and interactive data analysis with the help of machine learning are now being used to better establish the intricacies of the player load and injury relationship. In this article, we examine this recent research, describing the analyses and algorithms used, reporting the key findings, and comparing model fit. To date, the vast array of variables used in analysis as proxy indicators of player load, alongside differences in approach to key aspects of data treatment-such as response to data imbalance, model fitting, and a lack of multi-season data-limit a systematic evaluation of findings and the drawing of a unified conclusion. If, however, the limitations of current studies can be addressed, machine learning has much to offer the field and could in future provide solutions to the training load and injury paradox through enhanced and systematic analysis of athlete data.

EVIDENCE-BASED SPORTS MEDICINE TO PREVENT KNEE JOINT INJURY IN TRIPLE JUMP

ABSTRACT Introduction: Triple jump has high technical requirements. However, in recent years, the frequent knee injuries during the training of triple jump athletes have seriously impacted their training and competition levels. Objective: This article proposes a method for modeling knee joint injury caused by triple jump athletes overtraining based on an improved principal component analysis algorithm. It also studied the relationship between movement amplitude and sports injury. Methods: We obtained the optimal hyperplane showing data on the relationship between sports injury and joint motion range through the triple jump in the decision table. Then, the relationship model between the two was established. The article estimated the principal components of triple jump athletes’ knee joint injuries and established an accurate model relating the overtraining of these athletes and their knee joint injuries. Results: The accuracy of improved algorithm modeling is closer to that of physical examination outpatient records than to that of traditional algorithm modeling. Conclusion: The relationship model between triple jump injury and joint motion range was established using the improved algorithm. This model can greatly improve the accuracy of the relationship between the two and can effectively prevent triple jump injuries. Level of evidence II; Therapeutic studies - investigation of treatment results.

Construction of Correlation Analysis Model of College Students’ Sports Performance Based on Convolutional Neural Network

This paper proposes a network model recurrent fully connected network (RFC-Net) based on recurrent full convolution and polarization change. RFC-Net enriches the network by reconstructing and fine-tuning the fully convolutional network and adding recurrent convolutions to it. By studying the data mining technology of multidimensional association rules, based on the existing algorithms, this paper improves the shortcomings of the algorithms and realizes an efficient and practical method for data mining based on interdimensional multidimensional association rules. On the basis of mastering the actual student information, the effectiveness of the method is tested, and an employment analysis system based on association rules is established. Aiming at the fact that traditional grade prediction methods ignore the different influences of different behavioral characteristics on grades, and considering that behavioral data in different periods have different influences on student grades, the grade prediction problem is abstracted into a time series classification problem. The mechanism is combined with long short-term memory neural network to construct a performance prediction model based on Attention-BiLSTM. Experiments show that the prediction model proposed in this paper improves the accuracy and effectively improves the prediction quality compared with the logistic regression model with a better prediction effect in the traditional benchmark model and the long short-term memory neural network model without the introduction of the attention mechanism. Research shows that physical performance and academic performance are not contradictory. We must face up to the status of physical exercise in schools; as long as physical exercise is properly arranged, it can inspire students to form a spirit of unity, interaction, positivity, and perseverance in cultural studies.