Hasil untuk "Sports"

A. Guttmann

P. Chelladurai, S. D. Saleh

R. Fort, J. Quirk

C. L. Otis, B. Drinkwater, M. Johnson et al.

C. Gratton, I. Jones

Preface 1. What is Research? 2. Research Traditions 3. The Research Process 4. Research Questions, Aims and Objectives 5. Reviewing the Literature 6. Theories, Concepts and Variables in Sport Research 7. Research Designs 8. Collecting Data I - The Questionnaire Survey 9. Collecting Data II - Research Interviews 10. Collecting Data III - Unobtrusive Methods - Observation and Content Analysis 11. Collecting Data IV - Ethnographic Research in Sport 12. Analysing Data I - Quantitative Data Analysis 13. Analysing Data II - Qualitative Data Analysis 14. Writing a Research Report 15. Sports Research and the Internet 16. Student Issues 17. Bibliography 18. Journals publishing sports-related research 19. Glossary

Kumar Ashutosh, Chi Hsuan Wu, Kristen Grauman

Current large-scale video datasets focus on general human activity, but lack depth of coverage on fine-grained activities needed to address physical skill learning. We introduce SportSkills, the first large-scale sports dataset geared towards physical skill learning with in-the-wild video. SportSkills has more than 360k instructional videos containing more than 630k visual demonstrations paired with instructional narrations explaining the know-how behind the actions from 55 varied sports. Through a suite of experiments, we show that SportSkills unlocks the ability to understand fine-grained differences between physical actions. Our representation achieves gains of up to 4x with the same model trained on traditional activity-centric datasets. Crucially, building on SportSkills, we introduce the first large-scale task formulation of mistake-conditioned instructional video retrieval, bridging representation learning and actionable feedback generation (e.g., "here's my execution of a skill; which video clip should I watch to improve it?"). Formal evaluations by professional coaches show our retrieval approach significantly advances the ability of video models to personalize visual instructions for a user query.

刘明月, 靳沙沙, 王志勇, 付艳鑫, 张一唯, 倪隽, 武亮LIU Mingyue, JIN Shasha, WANG Zhiyong, FU Yanxin, ZHANG Yiwei, NI Jun, WU Liang

重症卒中后呼吸功能障碍常合并呼吸中枢驱动失调、呼吸泵衰竭及气道保护失效等多类复杂且异质的病理机制，传统康复模式难以满足个体化临床需求。本文综述了以精准评估为导向的整合性呼吸康复框架。该框架以多模态生理评估为基础，构建重症卒中患者呼吸功能特征图谱，进而为多学科团队制订个体化干预方案提供依据。尽管目前该领域仍面临高级别证据不足等挑战，但未来结合人工智能辅助决策与靶向神经调控技术的精准康复模式，或将为重症卒中呼吸康复的发展提供新思路。Respiratory dysfunction after severe stroke often involves multiple complex and heterogeneous pathological mechanisms, including dysregulated central respiratory drive, respiratory pump failure, and airway protection failure. Conventional rehabilitation models are difficult to meet individualized clinical demands. This paper reviews an integrated respiratory rehabilitation framework guided by precision assessment. Based on multi-modal physiological assessments, this framework constructs a respiratory function profile for patients with severe stroke, thereby providing evidence for the multi-disciplinary team to formulate individualized intervention protocols. Although the field still faces challenges such as insufficient high-level evidence, precision rehabilitation models, which integrate artificial intelligence-assisted decision-making and targeted neuromodulation technology, may provide new insights into the development of respiratory rehabilitation in severe stroke.

J. Lyle

Linfeng Dong, Yuchen Yang, Hao Wu et al.

We introduce RacketVision, a novel dataset and benchmark for advancing computer vision in sports analytics, covering table tennis, tennis, and badminton. The dataset is the first to provide large-scale, fine-grained annotations for racket pose alongside traditional ball positions, enabling research into complex human-object interactions. It is designed to tackle three interconnected tasks: fine-grained ball tracking, articulated racket pose estimation, and predictive ball trajectory forecasting. Our evaluation of established baselines reveals a critical insight for multi-modal fusion: while naively concatenating racket pose features degrades performance, a CrossAttention mechanism is essential to unlock their value, leading to trajectory prediction results that surpass strong unimodal baselines. RacketVision provides a versatile resource and a strong starting point for future research in dynamic object tracking, conditional motion forecasting, and multimodal analysis in sports. Project page at https://github.com/OrcustD/RacketVision

Dheeraj Khanna, Jerrin Bright, Yuhao Chen et al.

Multi-object tracking (MOT) in team sports is particularly challenging due to the fast-paced motion and frequent occlusions resulting in motion blur and identity switches, respectively. Predicting player positions in such scenarios is particularly difficult due to the observed highly non-linear motion patterns. Current methods are heavily reliant on object detection and appearance-based tracking, which struggle to perform in complex team sports scenarios, where appearance cues are ambiguous and motion patterns do not necessarily follow a linear pattern. To address these challenges, we introduce SportMamba, an adaptive hybrid MOT technique specifically designed for tracking in dynamic team sports. The technical contribution of SportMamba is twofold. First, we introduce a mamba-attention mechanism that models non-linear motion by implicitly focusing on relevant embedding dependencies. Second, we propose a height-adaptive spatial association metric to reduce ID switches caused by partial occlusions by accounting for scale variations due to depth changes. Additionally, we extend the detection search space with adaptive buffers to improve associations in fast-motion scenarios. Our proposed technique, SportMamba, demonstrates state-of-the-art performance on various metrics in the SportsMOT dataset, which is characterized by complex motion and severe occlusion. Furthermore, we demonstrate its generalization capability through zero-shot transfer to VIP-HTD, an ice hockey dataset.

Gulay Aras Bayram, Gizem Ergezen Sahin, Gizem Yilmaz et al.

Abstract Background With the advancement of technology, it is considered an important step to transfer assessment methods to the virtual environment as it provides individuals with the opportunity for equal feedback, improves test performance and allows for testing with maximum participation. The aim of this study was to evaluate the effects and differences between the classic Wingate Anaerobic Test (WAnT) and a virtual reality-based Wingate Test (VR-WAnT) on the test performance of athletes and to investigate their applicability to athletes. Methods Thirty male athletes aged between 18 and 25 years from professional football teams were included in the study. The athletes’ age, height, weight, total years of sport experience, scores on the system usability scale and satisfaction with the two different testing methods were assessed. A scenario covering all phases of the WAnT and requiring no external cues was prepared by the project team and integrated into the virtual reality headset. Athletes were first assessed using the classic WAnT in a controlled laboratory environment, and two days later the same athletes were assessed using the VR-WAnT in the same environment. Maximum power, minimum power, average power and fatigue index data from the test system were recorded for analysis. Results The results of the study showed no statistically significant differences in maximum power, minimum power, average power and fatigue index values between the two methods (p > 0.05). However, according to the satisfaction measurement, the results of the VR-WAnT were statistically significantly higher compared to the classic WAnT (p = 0.026). Conclusions VR-WAnT may be considered a promising alternative for anaerobic performance testing due to its potential to enhance user experience and satisfaction. It is also believed that the test may offer greater comfort for both practitioner and athletes, while introducing a novel dimension to physiotherapy and rehabilitation assessment processes. Trial registration NCT06661395 (Registration Date: 24 Oct 2024).

Farhad Rezazadeh, Shirin Aali, Fariborz Imani et al.

<i>Background and Objectives:</i> Chronic low back pain (CLBP) is associated with altered neuromuscular control. Dynamic Neuromuscular Stabilization (DNS) targets core–limb coordination; however, its specific impact on lower-limb electromyographic (EMG) activity during gait remains unclear. <i>Materials and Methods:</i> Fifty-five young adults with non-specific CLBP (pain ≥ 3 months with no identifiable specific pathology) completed the trial (overall mean age 23.7 ± 1.3 years). Participants were randomized to an 8-week DNS program or a control. Pre-/Post-intervention surface EMG during gait and clinical outcomes (VAS, ODI) were assessed. <i>Results:</i> Compared with control, DNS showed lower adjusted Post-test VAS (3.08 ± 0.25 vs. 6.13 ± 0.24; <i>ηp</i>² = 0.596) and ODI (15.73 ± 1.55% vs. 34.36 ± 1.52%; <i>ηp</i>² = 0.579). Directionally, DNS was associated with phase-specific EMG modulation: tibialis anterior during mid-stance was lower (<i>ηp</i>² = 0.137), rectus femoris during push-off was lower (<i>ηp</i>² = 0.119), biceps femoris during push-off was lower (<i>ηp</i>² = 0.168), and vastus medialis at heel-strike was higher (<i>ηp</i>² = 0.077) relative to control. Other muscle–phase pairs showed no adjusted between-group differences. <i>Conclusions:</i> An 8-week DNS program was associated with clinically meaningful reductions in pain and disability and with phase-specific changes in lower-limb EMG during gait. These findings support DNS as a promising rehabilitation option for young adults with CLBP; confirmation in larger trials with active comparators is warranted.

A. Junge, L. Engebretsen, M. Mountjoy et al.

Aayush Chaudhary

This study explores the impact of upselling on user engagement. We model users' deposit behaviour on the fantasy sports platform Dream11. Subsequently, we develop an experimental framework to evaluate the effect of upselling using an intensity parameter. Our live experiments on user deposit behaviour reveal decreased user recall with heightened upselling intensity. Our findings indicate that increased upselling intensity improves user deposit metrics and concurrently diminishes user satisfaction and conversion rates. We conduct robust counterfactual analysis and train causal meta-learners to personalise users' upselling intensity levels to reach an optimal trade-off point.

Shamik Bhattacharjee, Kamlesh Marathe, Hitesh Kapoor et al.

Fantasy sports, particularly fantasy cricket, have garnered immense popularity in India in recent years, offering enthusiasts the opportunity to engage in strategic team-building and compete based on the real-world performance of professional athletes. In this paper, we address the challenge of optimizing fantasy cricket team selection using reinforcement learning (RL) techniques. By framing the team creation process as a sequential decision-making problem, we aim to develop a model that can adaptively select players to maximize the team's potential performance. Our approach leverages historical player data to train RL algorithms, which then predict future performance and optimize team composition. This not only represents a huge business opportunity by enabling more accurate predictions of high-performing teams but also enhances the overall user experience. Through empirical evaluation and comparison with traditional fantasy team drafting methods, we demonstrate the effectiveness of RL in constructing competitive fantasy teams. Our results show that RL-based strategies provide valuable insights into player selection in fantasy sports.

Robin Schön, Daniel Kienzle, Rainer Lienhart

In this paper we introduce a new dataset containing instance segmentation masks for ten different categories of winter sports equipment, called WSESeg (Winter Sports Equipment Segmentation). Furthermore, we carry out interactive segmentation experiments on said dataset to explore possibilities for efficient further labeling. The SAM and HQ-SAM models are conceptualized as foundation models for performing user guided segmentation. In order to measure their claimed generalization capability we evaluate them on WSESeg. Since interactive segmentation offers the benefit of creating easily exploitable ground truth data during test-time, we are going to test various online adaptation methods for the purpose of exploring potentials for improvements without having to fine-tune the models explicitly. Our experiments show that our adaptation methods drastically reduce the Failure Rate (FR) and Number of Clicks (NoC) metrics, which generally leads faster to better interactive segmentation results.

Pilar Marqués-Sánchez, José Alberto Benítez-Andrades, María Dolores Calvo Sánchez et al.

Objectives: This study analyzed adolescent physical activity, its link to overweight, and the social network structure in group sports participants, focusing on centrality measures. Setting: Conducted in 11 classrooms across 5 schools in Ponferrada, Spain. Participants: Included 235 adolescents (49.4% female), categorized as normal weight or overweight. Methods: The Physical Activity Questionnaire for Adolescents (PAQ-A) assessed physical activity levels. Social network analysis evaluated centrality in varying contact degrees. Results: 30.2% were overweight. Males scored higher in PAQ-A and were more likely to engage in group sports. No significant correlation was found between physical activity and weight in the total sample. However, overweight females reported higher exercise levels. Centrality analysis showed gender differences; women in group sports had lower centrality, whereas men had higher. Conclusions: The study highlights the importance of gender and social network centrality in designing future strategies, considering peer interaction intensity

Aaron Baughman, Stephen Hammer, Rahul Agarwal et al.

We address the problem of scaling up the production of media content, including commentary and personalized news stories, for large-scale sports and music events worldwide. Our approach relies on generative AI models to transform a large volume of multimodal data (e.g., videos, articles, real-time scoring feeds, statistics, and fact sheets) into coherent and fluent text. Based on this approach, we introduce, for the first time, an AI commentary system, which was deployed to produce automated narrations for highlight packages at the 2023 US Open, Wimbledon, and Masters tournaments. In the same vein, our solution was extended to create personalized content for ESPN Fantasy Football and stories about music artists for the Grammy awards. These applications were built using a common software architecture achieved a 15x speed improvement with an average Rouge-L of 82.00 and perplexity of 6.6. Our work was successfully deployed at the aforementioned events, supporting 90 million fans around the world with 8 billion page views, continuously pushing the bounds on what is possible at the intersection of sports, entertainment, and AI.

Lachlan Thorpe, Lewis Bawden, Karanjot Vendal et al.

We present a transformer decoder based sports simulation engine, SportsNGEN, trained on sports player and ball tracking sequences, that is capable of generating sustained gameplay and accurately mimicking the decision making of real players. By training on a large database of professional tennis tracking data, we demonstrate that simulations produced by SportsNGEN can be used to predict the outcomes of rallies, determine the best shot choices at any point, and evaluate counterfactual or what if scenarios to inform coaching decisions and elevate broadcast coverage. By combining the generated simulations with a shot classifier and logic to start and end rallies, the system is capable of simulating an entire tennis match. We evaluate SportsNGEN by comparing statistics of the simulations with those of real matches between the same players. We show that the model output sampling parameters are crucial to simulation realism and that SportsNGEN is probabilistically well-calibrated to real data. In addition, a generic version of SportsNGEN can be customized to a specific player by fine-tuning on the subset of match data that includes that player. Finally, we show qualitative results indicating the same approach works for football.

Matias Gran-Henriksen, Hans Andreas Lindgaard, Gabriel Kiss et al.

This paper introduces Deep HM-SORT, a novel online multi-object tracking algorithm specifically designed to enhance the tracking of athletes in sports scenarios. Traditional multi-object tracking methods often struggle with sports environments due to the similar appearances of players, irregular and unpredictable movements, and significant camera motion. Deep HM-SORT addresses these challenges by integrating deep features, harmonic mean, and Expansion IOU. By leveraging the harmonic mean, our method effectively balances appearance and motion cues, significantly reducing ID-swaps. Additionally, our approach retains all tracklets indefinitely, improving the re-identification of players who leave and re-enter the frame. Experimental results demonstrate that Deep HM-SORT achieves state-of-the-art performance on two large-scale public benchmarks, SportsMOT and SoccerNet Tracking Challenge 2023. Specifically, our method achieves 80.1 HOTA on the SportsMOT dataset and 85.4 HOTA on the SoccerNet-Tracking dataset, outperforming existing trackers in key metrics such as HOTA, IDF1, AssA, and MOTA. This robust solution provides enhanced accuracy and reliability for automated sports analytics, offering significant improvements over previous methods without introducing additional computational cost.