Search Results (199)

Human activity recognition (HAR) systems are essential in healthcare, surveillance, and sports analytics, enabling automated movement analysis. This research presents a novel HAR system combining graph structures with deep neural networks to capture both spatial and temporal patterns in activities. While CNN-based models excel at spatial feature extraction, they struggle with temporal dynamics, limiting their ability to classify complex actions. To address this, we applied the Firefly Optimization Algorithm to fine-tune the hyperparameters of both the graph-based model and a CNN baseline for comparison. The optimized graph-based system, evaluated on the UCF101 and Kinetics-400 datasets, achieved 88.9% accuracy with balanced precision, recall, and F1-scores, outperforming the baseline. It demonstrated robustness across diverse activities, including sports, household routines, and musical performances. This study highlights the potential of graph-based HAR systems for real-world applications, with future work focused on multi-modal data integration and improved handling of occlusions to enhance adaptability and performance. Full article

The rapid rise of social media and widespread Internet access have contributed significantly to the global popularity of e-sports. However, while popular e-sports attract considerable attention, niche e-sports remain underexplored, limiting user discovery and engagement. This paper proposes a Twitter-based recommendation system that uses advanced data management and processing techniques to address the challenge of identifying and recommending both popular and niche e-sports. The system analyzes social media metadata, including user IDs, followers, followees, engagements, and impressions, to calculate two critical metrics: popularity and satisfaction. Based on the combination of these metrics, the system calculates overall scores for each e-sports and generates two distinct rankings: one for popular and another for niche e-sports. The proposed system reflects the application of data-driven methodologies and social network analysis in creating recommendations that meet diverse user preferences, highlighting the relevance of data processing technologies in personalized content delivery. Experimental evaluations, using a dataset derived from Twitter hashtags (#) representing 30 target e-sports in 2022, demonstrate the system’s effectiveness in capturing the emerging dynamics in e-sports and providing actionable insights for diverse user preferences. This study highlights the potential of SNS-based technologies to advance data processing, analysis, and application within the e-sports ecosystem. Full article

Background/Objectives: Urinary incontinence (UI) significantly impacts quality of life, with varying prevalence in women depending on factors such as age, childbirth, and type of sport practiced. This study compared the prevalence, types, and severity of urinary incontinence (UI) between professional female soccer players and sedentary students, analyzing its relation to playing position and competitive level. Methods: A descriptive, observational, and analytical cross-sectional study was conducted, assessing the prevalence, severity, and types of UI among 235 nulliparous professional female soccer players (experimental group, EG) and 252 sedentary female students (control group, CG). Data were collected using the short version of the International Consultation on Incontinence Questionnaire (ICIQ-SF). Statistical analyses included Fisher’s exact test to compare prevalence rates. Results: The findings revealed that 35% of soccer players and 31% of sedentary students reported experiencing UI. Stress urinary incontinence (SUI) was the most prevalent type in both groups, affecting 26% of soccer players and 14% of sedentary students, while mixed UI was more frequent among sedentary women (17%) (p < 0.05). No significant differences were observed in UI prevalence based on playing position or competitive level (p ≥ 0.05). However, female soccer players exhibited a significantly higher prevalence of UI during physical exertion or exercise compared to the control group (p ≤ 0.001), suggesting that high-impact sports may contribute to pelvic floor dysfunction. Additionally, 23.8% of soccer players reported mild-to-moderate UI severity. Conclusion: Female soccer players showed higher UI prevalence during exercise, underscoring the need for targeted interventions like pelvic floor training. Full article

Electronic sports (esports) and research on this emerging field are interdisciplinary in nature. By extension, it is essential to understand how to standardize and structure training with the help of existing tools, developed over years of research in sports sciences and informatics. Our goal for this work is to review the available literature in esports research, focusing on sports sciences (training, periodization, planning, and career stages) and software (training tools, visualization, analytics, and feedback systems). To verify the existing sources, we applied the framework of a scoping review to address the search from multiple scientific databases with further local processing. We conclude that the current research on esports has mainly focused on describing and modeling performance metrics that span over multiple fragmented research areas (psychology, nutrition, informatics). However, these building blocks have not been assembled into a well-functioning theory of performance in esports by, e.g., providing exercise regimes or methods of periodization for esports. Full article

This manuscript presents a multidisciplinary study that proposes a methodology for delineating and categorizing vulnerability at rockfall risk areas to avoid human injuries and infrastructure damage caused by rockfalls. The presented workflow includes (i) classical geological mapping, (ii) the interpretation of high-resolution satellite data for observing the spatial distribution of fallen boulders, (iii) analytical hierarchy processing of spatial information within a Geographical Information System (GIS) platform, (iv) close-range remote sensing campaigns with Unmanned Aerial Systems (UASs), and (v) integrated simulation of rockfall events. This methodology was applied to Kalymnos Island, which belongs to the Dodecanese Islands complex of the southeastern Aegean Sea in Greece. It is characterized by unique geomorphological features, including extensive vertical limestone cliffs that span the island. These cliffs make it one of the world’s most densely concentrated areas for sport climbing. The results highlighted the areas that the local authorities need to focus on and suggested measures for increasing the safety of climbers and infrastructure. Full article

This paper presents a novel approach to predicting shot accuracy in badminton by analyzing Quiet Eye (QE) metrics such as QE duration, fixation points, and gaze dynamics. We develop a neural network model that combines visual data from eye-tracking devices with biomechanical data such as body posture and shuttlecock trajectory. Our model is designed to predict shot accuracy, providing insights into the role of QE in performance. The study involved 30 badminton players of varying skill levels from the Chinese Swimming Club in Singapore. Using a combination of eye-tracking technology and motion capture systems, we collected data on QE metrics and biomechanical factors during a series of badminton shots for a total of 750. Key results include: (1) The neural network model achieved 85% accuracy in predicting shot outcomes, demonstrating the potential of integrating QE metrics with biomechanical data. (2) QE duration and onset were identified as the most significant predictors of shot accuracy, followed by racket speed and wrist angle at impact. (3) Elite players exhibited significantly longer QE durations (M = 289.5 ms) compared to intermediate (M = 213.7 ms) and novice players (M = 168.3 ms). (4) A strong positive correlation (r = 0.72) was found between QE duration and shot accuracy across all skill levels. These findings have important implications for badminton training and performance evaluation. The study suggests that QE-based training programs could significantly enhance players’ shot accuracy. Furthermore, the predictive model developed in this study offers a framework for real-time performance analysis and personalized training regimens in badminton. By bridging cognitive neuroscience and sports performance through advanced data analytics, this research paves the way for more sophisticated, individualized training approaches in badminton and potentially other fast-paced sports. Future research directions include exploring the temporal dynamics of QE during matches and developing real-time feedback systems based on QE metrics. Full article

As an important carrier of sports services and the main force participating in the “short board” project of urban development, the renovation and upgrading of old stadiums have become an important trend for the sustainable development of venues in the context of urban renewal, consumption upgrading, and national fitness. However, owing to the complexity of the transformation process, the probability of safety accidents continues to increase, posing a serious threat to national property security. In order to reasonably evaluate the safety of the stadium renovation construction process and reduce the incidence of accidents in the renovation project, this study proposed a safety evaluation model for the stadium renovation construction based on the combination weighting extension model. First, according to the 5M1E theory, 27 influencing factors were selected, a safety evaluation index system for stadium reconstruction construction was constructed, and the safety evaluation grade of the index was quantified. Second, based on the analytic hierarchy process (AHP) and improved entropy weight method, the combination weight of the index was determined, and a safety evaluation model was constructed using the matter–element extension theory. Finally, the established evaluation model was applied to the example of stadium renovation, and the construction safety level of the renovation project was obtained. The research results showed that the model has strong operability, and the evaluation results are reasonable and reliable, providing a new concept for the safety control of stadium reconstruction construction. Full article

The process of manually annotating sports footage is a demanding one. In American football alone, coaches spend thousands of hours reviewing and analyzing videos each season. We aim to automate this process by developing a system that generates comprehensive statistical reports from full-length football game videos. Having previously demonstrated the proof of concept for our system, here, we present optimizations to our preprocessing techniques along with an inventive method for multi-person event detection in sports videos. Employing a long short-term memory (LSTM)-based architecture to detect the snap in American football, we achieve an outstanding LSI (Levenshtein similarity index) of 0.9445, suggesting a normalized difference of less than 0.06 between predictions and ground truth labels. We also illustrate the utility of snap detection as a means of identifying the offensive players’ assuming of formation. Our results exhibit not only the success of our unique approach and underlying optimizations but also the potential for continued robustness as we pursue the development of our remaining system components. Full article

This research addresses the challenge of enhancing energy efficiency in public buildings while maintaining or improving occupant comfort. With stricter modern energy regulations, many older facilities, such as sports halls built between 1960 and 1980, face the need for renovation to meet current standards. The central research question investigates what measures can be implemented to improve the energy efficiency of sports halls without compromising comfort for the occupants. This study examines strategies, techniques, and possibilities for optimizing energy performance during the rehabilitation of universal sports halls within sports centers. It includes a theoretical and analytical evaluation of various measures in line with existing regulations and thermal comfort requirements. This research uses simulation software, the Integrated Environmental Solutions Virtual Environment, to model different Passive House measures applied to a case study of a sports center built in 1976 in Belgrade. This study provides practical guidelines for enhancing thermal insulation on the building’s envelope to achieve energy savings. The application of these measures demonstrates that significant energy savings can be realized by focusing on specific sections of the building, such as the administrative areas, rather than the entire facility. The findings offer valuable insights into energy-optimization strategies for existing sports facilities, highlighting the practical application of measures to improve energy performance in a real-world context. The results contribute to the development of effective renovation practices for older sports buildings, ensuring they meet modern energy efficiency standards while maintaining optimal comfort for users. Full article

In the dynamic realm of golf, where every swing can make the difference between victory and defeat, the strategic selection of golf clubs has become a crucial factor in determining the outcome of a game. Advancements in artificial intelligence have opened new avenues for enhancing the decision-making process, empowering golfers to achieve optimal performance on the course. In this paper, we introduce an AI-based game planning system that assists players in selecting the best club for a given scenario. The system considers factors such as distance, terrain, wind strength and direction, and quality of lie. A rule-based model provides the four best club options based on the player’s maximum shot data for each club. The player picks a club, shot, and target and a probabilistic classification model identifies whether the shot represents a birdie opportunity, par zone, bogey zone, or worse. The results of our model show that taking into account factors such as terrain and atmospheric features increases the likelihood of a better shot outcome. Full article

Introduction: Performance analysis is essential for coaches and a topic of extensive research. The advancement of technology and Artificial Intelligence (AI) techniques has revolutionized sports analytics. Aim: The primary aim of this article is to present a robust, explainable machine learning (ML) model that identifies the key factors that contribute to securing one of the top three positions in the standings of the French Ligue 1, ensuring participation in the UEFA Champions League for the following season. Materials and Methods: This retrospective observational study analyzed data from all 380 matches of the 2022–23 French Ligue 1 season. The data were obtained from the publicly-accessed website “whoscored” and included 34 performance indicators. This study employed Sequential Forward Feature Selection (SFFS) and various ML algorithms, including XGBoost, Support Vector Machine (SVM), and Logistic Regression (LR), to create a robust, explainable model. The SHAP (SHapley Additive Explanations) model was used to enhance model interpretability. Results: The K-means Cluster Analysis categorized teams into groups (TOP TEAMS, 3 teams/REST TEAMS, 17 teams), and the ML models provided significant insights into the factors influencing league standings. The LR classifier was the best-performing classifier, achieving an accuracy of 75.13%, a recall of 76.32%, an F1-score of 48.03%, and a precision of 35.17%. “SHORT PASSES” and “THROUGH BALLS” were features found to positively influence the model’s predictions, while “TACKLES ATTEMPTED” and “LONG BALLS” had a negative impact. Conclusions: Our model provided satisfactory predictive accuracy and clear interpretability of results, which gave useful information to stakeholders. Specifically, our model suggests adopting a strategy during the ball possession phase that relies on short passes (avoiding long ones) and aiming to enter the attacking third and the opponent’s penalty area with through balls. Full article

This research investigates clutch performance in the National Basketball Association (NBA) with a focus on the final minutes of contested games. By employing advanced data science techniques, we aim to identify key factors that enhance winning probabilities during these critical moments. The study introduces the Estimation of Clutch Competency (EoCC) metric, which is a novel formula designed to evaluate players’ impact under pressure. Examining player performance statistics over twenty seasons, this research addresses a significant gap in the literature regarding the quantification of clutch moments and challenges conventional wisdom in basketball analytics. Our findings deal valuable insights into player efficiency during the final minutes and its impact on the probabilities of a positive outcome. The EoCC metric’s validation through comparison with the NBA Clutch Player of the Year voting results demonstrates its effectiveness in identifying top performers in high-pressure situations. Leveraging state-of-the-art data science techniques and algorithms, this study analyzes play data to uncover key factors contributing to a team’s success in pivotal moments. This research not only enhances the theoretical understanding of clutch dynamics but also provides practical insights for coaches, analysts, and the broader sports community. It contributes to more informed decision making in high-stakes basketball environments, advancing the field of sports analytics. Full article

The impact of sporting events on local economies and their spatial distribution is a topic of active policy debate. This study adds to the discussion by examining granular cellphone location data to assess the spillover effects of Major League Baseball (MLB) games in a major US city. Focusing on the 2019 season, we explore granular geospatial patterns in mobility and consumer spending on game days versus non-game days in the Saint Louis region. Through density-based clustering and hotspot analysis, we uncover distinct spatiotemporal signatures and variations in visitor affluence across different teams. This study uses features like game day characteristics, location data (latitude and longitude), business types, and spending data. A significant finding is that specific spatial clusters of economic activity are formed around the stadium, particularly on game days, with multiple clusters identified. These clusters reveal a marked increase in spending at businesses such as restaurants, bars, and liquor stores, with revenue surges of up to 38% in certain areas. We identified a significant change in spending patterns in the local economy during games, with results varying greatly across teams. Notably, the XGBoost model performs best, achieving a test R² of 0.80. The framework presented enhances the literature at the intersection of urban economics, sports analytics, and spatial modeling while providing data-driven actionable insights for businesses and policymakers. Full article

Introduction: Football analysis is an applied research area that has seen a huge upsurge in recent years. More complex analysis to understand the soccer players’ or teams’ performances during matches is required. The objective of this study was to prove the usefulness of the expected goals on target (xGOT) metric, as a good indicator of a soccer team’s performance in professional Spanish football leagues, both in the women’s and men’s categories. Method: The data for the Spanish teams were collected from the statistical website Football Reference. The 2023/24 season was analyzed for Spanish leagues, both in the women’s and men’s categories (LigaF and LaLiga, respectively). For all teams, the following variables were calculated: goals, possession value (PV), expected goals (xG) and xGOT. All data obtained for each variable were normalized by match (90 min). A descriptive and correlational statistical analysis was carried out. Results: In the men’s league, this study found a high correlation between goals per match and xGOT (R² = 0.9248) while in the women’s league, there was a high correlation between goals per match (R² = 0.9820) and xG and between goals per match and xGOT (R² = 0.9574). Conclusions: In the LaLiga, the xGOT was the best metric that represented the match result while in the LigaF, the xG and the xGOT were the best metrics that represented the match score. Full article

Background: Basketball is a high-intensity sport, which includes actions such as jumping, changes of direction, accelerations, and decelerations, which generates fatigue situations that may increase the risk of injury. Specifically, the joints at greatest risk are the ankle and knee, with ankle sprains and anterior cruciate ligament (ACL) tears being the most prevalent injuries. There are several strategies aimed at reducing the incidence, based on training methods or other prophylactic measures. Therefore, the purpose of the study is to perform a systematic review of the different injury prevention strategies in competitive-level basketball players with respect to general injuries, ankle sprains, and ACL injuries. Methods: For this purpose, the PRISMA methodology was applied, performing a search in three databases (PubMed, SPORTDiscus, and Cochrane) between 25 September 2023 and 8 October 2023. Results: A total of 964 articles were identified, out of which 283 were duplicates and 644 were discarded. Out of the remaining 37, 23 were excluded because they did not meet the inclusion criteria; therefore, 14 articles were finally included. With respect to general injuries, 8 out of 14 studies reviewed them. Concerning ankle sprains, 7 studies specifically analyzed them. Finally, 3 studies focused on ACL injuries. Conclusions: Training programs that combine different contents, known as neuromuscular training, including strength work, stabilization or core, mobility, and agility are the most effective for both general injuries and ACL injuries. For ankle sprains, the most effective measures are training programs based on analytical ankle stability exercises and the use of ankle braces. Adherence to prevention programs is essential, so they can be included as part of the warm-up. Other strategies such as training load control, functional assessment, or rule modification are not used in the included articles, so their effectiveness as prophylactic methods could not be justified. Full article