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A073: Exercise and Artificial Intelligence: Prediction and Prevention of Sports Injury Based on Big Data
No full textThe incidence of sports-related injuries continues to rise, yet traditional prevention strategies, such as empirical training modifications and static biomechanical assessments, exhibit significant limitations in dynamic risk prediction and personalized intervention. This study aims to leverage the integration of artificial intelligence (AI) and big data technologies to establish a dynamic, precise system for sports injury prediction and prevention, thereby reducing injury rates and enhancing rehabilitation strategies. A comprehensive sports injury feature database was developed by integrating multi-source data, including physiological parameters collected from wearable devices, biomechanical data from video motion capture systems, and clinical rehabilitation records. Machine learning algorithms, including Random Forest and Long Short-Term Memory (LSTM) neural networks, were employed to identify and analyze key risk factors for injuries, such as peak joint load and muscle fatigue index. Model performance was further optimized through A/B testing and cross-validation. Longitudinal cohort tracking of athletes was conducted, and the effectiveness of intervention measures was validated through a real-time feedback system. The AI-driven model demonstrated a predictive accuracy of 89.7% (AUC = 0.93), representing a substantial improvement over traditional methods, which achieved only 72.4% accuracy. For chronic injuries such as ankle instability, personalized intervention protocols led to a 34% reduction in recurrence rates. The exoskeleton-assisted rehabilitation system enhanced gait correction efficiency by 41% while simultaneously reducing recovery time. Furthermore, multimodal data fusion techniques successfully identified 15 high-risk movement patterns, providing objective, data-driven insights to optimize training regimens. The integration of AI and big data technologies facilitates the early detection and precise prevention of sports injuries through dynamic monitoring and sophisticated pattern recognition. Despite these advances, challenges related to data privacy, interdisciplinary collaboration, and algorithm transparency remain. Future research should focus on expanding the diversity of sample populations and exploring the potential application of AI in sports psychology interventions. This innovative technological framework offers transformative solutions for injury prevention and management in both elite athletics and general fitness contexts
A181: Research on Operation Risk Identification and Emergency Management Model of Large Stadiums Based on AI
No full textLarge-scale stadiums and gymnasiums are important material carriers for building a higher level of public service system of national fitness and promoting the construction of a strong sports country. At present, the scale of China\u27s sports competition and performance industry continues to expand, and the number of various commercial activities is increasing. Under this background, the safe operation of stadiums and gymnasiums is particularly important. This study constructs a model of risk identification and emergency management of large-scale stadiums and gymnasiums by introducing artificial intelligence technology, and explores a new mode of stadium management, aiming at improving the level of risk management of large-scale stadiums and gymnasiums. Method: This study employs a comprehensive approach, integrating literature review, expert interviews, and Importance-Performance Analysis (IPA) to identify risk factors in the operation of large sports venues and assess their significance and priority. Subsequently, a model-building approach was utilized to develop an intelligent operational and emergency management model for large sports venues through empirical analysis. This study identifies risk factors such as market competition, operating costs, industry standards, insurance system, management system, staffing, building materials quality, natural disasters, and constructs a risk identification and emergency management model for large-scale stadiums and gymnasiums. Meantime, establish a data collection and processing platform through the integration of intelligent devices and sensors and other technical means to provide timely access to dynamic information of venues, to provide a decision-making basis for people flow scheduling, resource allocation, and so on. Construct a risk prediction and early warning system, using the collected multi-dimensional information, combined with the actual situation of the venue, to quickly identify abnormal behavior, and automatically generate the optimal emergency response plan. This study provides a scientific and intelligent solution for the operation and safety management of large-scale stadiums and gymnasiums. In the future, it is necessary to strengthen the application and research and development of AI, explore more intelligent and automated management methods, continuously optimize the algorithm model, improve the prediction accuracy and decision support capability; Improve the data collection and analysis system, establish a data sharing mechanism, promote data circulation and collaboration among different departments. Attach importance to personnel training in the field of stadium operation and risk management, improve the professional quality and skill level of the team through training and education, and provide strong support for the high-quality development of large stadiums
A080: Research on Intelligent Practical Path of Extracurricular Sports Models in the Context of AI Empowerment
No full textThis study aims to explore and optimize the teaching, practicing, and competing sports teaching model by integrating artificial intelligence (AI) technology. It focuses on building a new intelligent ecosystem for sports education, emphasizing the connection between teaching, the bridging role of practicing, and the efficiency of regular competition to enhance students\u27 sports skills and literacy. Through AI, the study enables personalized teaching content, intelligent practice feedback, and automated competition management. This approach provides innovative ideas and theoretical support for school physical education, promotes the deep integration of learning, practicing, and competing, and strengthens school sports education in the new era. This paper explores the new path of AI-enabled extracurricular physical activity practice from the perspective of teaching, practicing, and competing by means of literature and logical analysis to lay a theoretical foundation for promoting the development of school physical education. Currently, there is a formalism erosion in extracurricular activities in schools, single and boring organizations, and a lack of activity resources, which seriously impede the integrated development of learning, training, and competing and affect students\u27 interest and the effect of physical exercise. The AI intelligent sports program in this study integrates artificial intelligence and competitive elements, which can greatly enhance students\u27 motivation to learn and practice. At the same time, the system can realize the daily exercise and competition, flexibly arrange the study and practice plan, combine online and offline, enrich the after-school life, and cultivate sports habits. Secondly, the AI system provides comprehensive data support to ensure fair and accurate teaching evaluation. Finally, AI Smart Sports also saves teaching resources, reduces teachers\u27 burden, and improves teaching efficiency and quality through intelligent teaching assistance. Since the AI intelligent sports system is in the initial development stage, there are still deficiencies, so in order to better solve the dilemma of the problem put forward the following suggestions: (I) Innovate the content of AI learning and practicing, the core of the AI intelligent sports system lies in the diversified learning and practicing content, and the need to continue to update sports knowledge. (II) Build an information-sharing platform to promote cross-school communication, integrate teaching resources, and stimulate dynamic updating potential. (III) Implement stratified optimization management, staggered use of resources, and intelligent scheduling to improve efficiency. (IV) Establish a quality assessment system, collect user feedback, regularly evaluate system effectiveness, and continuously optimize content to ensure user experience
A154: Effects of Stroboscopic Vision on Static Postural Control in Individuals with Chronic Ankle Instability
No full textChronic ankle instability (CAI) is a syndrome characterized by persistent dysfunction resulting from repeated ankle sprains, often leading to impaired postural control in affected individuals. Although rehabilitation training on unstable surfaces is effective for patients with CAI, it does not sufficiently reduce their reliance on visual input. While eye-closure training can restrict visual input, it poses safety risks and exhibits low compliance rates. Stroboscopic vision may offer a safe and effective alternative; however, its efficacy in reducing visual reliance among CAI patients on unstable surfaces remains unclear. Therefore, this study aimed to assess the impact of stroboscopic vision on static postural control in CAI patients on unstable surfaces. Method: Twenty-five individuals with CAI (18 females and 7 males, age: 21.2±1.2 years, height: 167.9±10.2 cm, body mass: 62.3±10.8 kg) were recruited from Shandong Sport University. Their static postural control was assessed across three visual conditions: eyes-open (EO), eyes-closed (EC), and strobe vision (SV). Static postural control was evaluated during single-limb stance on a padded sponge for 10 seconds and represented by root mean square (RMS) of the plantar center of pressure (CoP), total CoP displacement velocity (CoP_Vel_Total), and the 95% confidence ellipse area (95% Area). One-way repeated measures ANOVA was used to analyze data. Significant differences were observed in COP_RMS_ML (p ＜0.05), CoP_Vel_Total (p ＜0.05), and 95% Area (p ＜0.05) across visual conditions. Compared to the EO condition, the 95% Area and CoP_Vel_Total significantly increased in individuals with CAI under both SV and EC conditions on an unstable surface. COP_RMS_ML significantly increased under the SV condition compared to the EO condition. No significant difference in COP_RMS_AP was observed across visual conditions (p ＞0.05). This study revealed a significant impact of visual interference on static postural control in people with CAI. Individuals with CAI exhibit a strong reliance on visual input to maintain balance, and visual deprivation or distortion may exacerbate postural instability, particularly in the medial-lateral direction. Consequently, rehabilitation programs should integrate visual challenge training, such as stroboscopic or eye-closure exercises, to enhance postural control and mitigate the risk of re-injury
A038: Research on Artificial Intelligence Promoting Sports and Health Development
No full textWith the rapid advancement of artificial intelligence (AI) technology, its application in the fields of sports and health promotion has become increasingly widespread, bringing revolutionary changes to areas such as sports training, performance analysis, health monitoring, and management. The application of AI not only enhances the efficiency of research and practice but also provides new possibilities for personalized services and precise interventions. This study aims to explore the impact of the AI era on sports and health promotion, analyzing its current applications and future development trends in these fields. Method: Literature Review: Systematically collect, organize, and analyze relevant literature to understand the current applications, development trends, and existing challenges of AI in sports and health promotion. Case Analysis: Select typical cases for in-depth analysis to explore the effectiveness and challenges of AI technology in specific sports training and health management applications. Empirical Research: Collect and analyze real-world data to validate the effectiveness and impact of AI technology in sports and health promotion. AI technology has achieved significant results in areas such as sports performance analysis, personalized training program development, and injury prevention and rehabilitation. technology assists in health data monitoring and analysis, disease prediction and prevention, and personalized health management by enabling real-time collection and analysis of users\u27 physiological data. The use of AI technology has also raised a series of ethical issues, primarily concerning data privacy and security, algorithmic bias and fairness, and human-machine interaction and accountability. Multimodal data integration and intelligent analysis will become mainstream, allowing AI systems to provide more comprehensive and accurate analysis and predictions. The integration of virtual reality (VR) and augmented reality (AR) technologies with AI will become a core component of future health management. In the field of sports, AI technology demonstrates significant potential in areas such as performance analysis, personalized training, and injury prevention. In health promotion, AI-driven health monitoring, disease prediction, and personalized management are transforming traditional health management models. However, the application of these technologies also brings ethical challenges related to data privacy, algorithmic bias, and accountability, which require collective efforts to address
A001: Electronic Health Interventions on Promoting Physical Activity in Patients with Type 2 Diabetes: A Meta-Analysis
No full textThe prevalence of diabetes mellitus is increasing year over year, and its high number of complications and disability rates pose a significant challenge to global public health. Electronic health has achieved better efficacy in patients with type 2 diabetes, but research in promoting exercise in diabetes is incomplete. A computerized search of Web of science, PubMed, The Cochrane Library, and Embase databases was conducted to retrieve randomized controlled trials (RCTs) of e-health interventions for physical activity in patients with type 2 diabetes. The risk of bias of the included literature was assessed according to ROB 2.0. Heterogeneity of study results was detected by I², and subgroup meta-analysis were used to find sources of heterogeneity. Sensitivity analyses assessed the stability of results. Meta-analysis was performed using Stata 15.0. A total of 14 RCTs with 1,385 patients were included. Meta-analyses showed that the test group significantly improved physical activity (SMD: 0.35; 95% Cl: 0.22, 0.49) and reduced glycated hemoglobin (WMD：0.74；95%Cl：0.34，1.14), BMI (WMD: 0.74; 95% Cl: 0.36, 1.12), and waist circumference (SMD: 0.49; 95% Cl: 0.05, 0.93; all P’s \u3c 0.05) but did not significantly reduce the levels of blood lipids (Cholesterol, Triglyceride, HDL-c, and LDL-c; all P’s \u3e 0.05). E-health interventions can improve physical activity and reduce glycated hemoglobin, BMI and waist circumference in patients with type 2 diabetes mellitus. These conclusions still need to be confirmed with larger sample sizes and high-quality studies
A149: Research on the Application of Artificial Intelligence in Football
No full textWith the development of artificial intelligence technology in the sports field, the role of sports data information is becoming increasingly significant. Specifically, in the application of football, artificial intelligence technology is used to scientifically mine data from human sports activities, analyze the patterns of training and competition, intelligently prevent sports injuries, analyze the competitive state of athletes, and form scientific training plans, thereby enhancing the performance of competitive sports and achieving scientific fitness and proactive health. The core concept of artificial intelligence technology is to mine data and explore the patterns within it to explain current phenomena and predict the future. Method: Using keywords such as artificial intelligence and football , multiple databases, including CNKI and Google Scholar, were searched. The collected literature was analyzed to systematically summarize the current cutting-edge developments of artificial intelligence in football. Secondly, we visited websites such as zone7 and sportlogiq that use artificial intelligence for sports analysis to understand the practical application of artificial intelligence in football. Additionally, we conducted online interviews with practitioners and researchers using the interview method. These interviews aimed to gain a deeper understanding of the current development direction, technical challenges, and application level of artificial intelligence in football. Through the analysis of collected literature and surveys, this study found that artificial intelligence in football data mining has the following advantages: (1) The types of collected data are increasing. (2) The ability to analyze data has significantly improved. (3) The ability to explore the deep connections between different data has significantly improved. (4) The real-time nature of data collection has improved. (5) The accuracy of data collection is. This study, starting from multiple dimensions such as physical fitness, skills, tactics, psychology, load, and injuries, found that the application of artificial intelligence in football mainly includes: (1) Analyzing the patterns of training and competition. (2) Intelligent prevention of sports injuries. (3) Analyzing the competitive state of athletes. (4) Personalized arrangement of training plans. (5) Optimization of tactical analysis. Efforts should be made to further strengthen the use of big data technology to mine sports data, further clarify the inherent laws in sports training, and improve the application of big data in sports
A288: The Influence of Physical Literacy on Physical Activity: The Mediating Role of Sport Commitment
No full textPhysical inactivity has become a global public crisis. The physical activity level of college students has decreased significantly worldwide, while physical literacy as a potential way to promote physical activity is receiving attention. However, the mechanism by which physical literacy promotes physical activity remains unclear. The purpose of this study was to explore the relationship between physical literacy and physical activity and the mediating role of sport commitment. Method: 824 college students from Changchun, Jilin Province, China, participated in this study (Mage=21) using the Perceived Physical Literacy Instrument, International Physical Activity Questionnaire Short Form, and Sport Commitment Questionnaire. Pearson correlation, independent sample t-tests, and linear regression analysis were employed to examine the associations between the variables. Physical literacy, physical activity, and sport commitment were significantly correlated. Physical literacy has a direct positive effect on both sport commitment and physical activity, and sport commitment also has a positive effect on physical activity. Sport commitment mediates the relationship between physical literacy and physical activity among Chinese college students. The physical activity of Chinese college students is not only influenced by physical literacy but also mediated by sport commitment. Therefore, improving the physical literacy of college students and promoting sport commitment are of great importance for improving the physical activity of college students. Future research should pay more attention to physical activity interventions with physical literacy and sport commitment as entry points
A074: VR / AR Technology Innovation in Sports Training: From Action Capture to Multimodal Feedback
No full textTraditional sports training is constrained by limited environmental controllability, delayed feedback, and insufficient motion quantification. VR/AR technologies offer immersive interactions and multimodal feedback systems, enabling high-fidelity scenarios, real-time motion capture, and precise quantitative analysis for motor skill development. This study systematically examines technological innovations in motion capture and multimodal feedback within VR/AR frameworks and evaluates their efficacy. The research integrate motion capture technologies (e.g., Kinect V2 skeletal tracking with deep learning algorithms) and multimodal feedback systems into a three-stage framework: 1) A hybrid inertial-visual motion capture system achieving joint angle modeling with an error margin \u3c 1.5°; 2) Multimodal feedback design incorporating visual overlays (motion trajectory projection), tactile vibrations (HaptX glove force feedback), and spatial audio cues; 3) Real-time biomechanical analysis modules assessing cognitive-motor coordination via synchronized eye-tracking and electromyography. Experimental trials involved soccer/tennis athletes and rehabilitation patients in comparative training studies. 1) VR-trained soccer athletes exhibited 23% faster tactical decision-making and 18% higher action anticipation accuracy than the control group; 2) Tactile-visual multimodal feedback improved tennis serve standardization by 31%, outperforming single-modality interventions; 3) AR-integrated gait rehabilitation increased patients\u27 POMA balance scores by 42%, with 92% training adherence. However, neck fatigue caused by device weight reduced long-term compliance for 15% of users. VR/AR technologies enable quantifiable, personalized, and context-adaptive sports training through high-precision motion capture and multimodal feedback. Their core value lies in transcending physical space limitations and accelerating neuro-muscular memory formation. Future research should prioritize lightweight device design and 5G-edge computing-enabled distributed training systems. While multimodal synergy is validated, feedback modality prioritization requires task-specific optimization—e.g., spatial auditory cues for team sports versus tactile precision enhancement for fine motor tasks
A004: Effects of Inspiratory Muscle Warm-Up on Muscle Oxygenation, Perceived Exertion, and Prefrontal Cortex Activation
No full textWith the rapid increase in rowing training load, high subjective load may force athletes to use more nerve impulses and cognitive resources. However, the neural resources of athletes are limited, and being in this state for a long time may have a negative effect, such as reducing information processing ability stability, and even non-contact injury. The primary aim of this study was to investigate the effects of a respiratory muscle warm-up intervention on oxygen saturation in the prefrontal lobe and muscles of athletes. A total of 54 participants were recruited for the study, with an average age of 21.35 years. were randomly assigned to one of three groups: IMW, placebo, or blank control. A portable muscle oximeter (Moxy, USA) was employed to monitor the muscle oxygen saturation level throughout the training period. The degree of activation of the prefrontal cortex (PFC) was quantified by means of an oxygenation monitoring system (OctaMon). Concurrently, the CR10 scale was utilized to assess perceived exertion. Following the intervention, a significant difference in CR10 was observed between the various groups. A series of multiple comparisons demonstrated that the CR10 in the IMW group exhibited a significantly lower value than that observed in the placebo and control groups (p \u3c 0.001). Once the baseline values from the pretest had been accounted for, a significant difference in oxygen saturation was observed between the different groups in the bilateral PFC. Subsequent post hoc multiple comparisons revealed that the HbO2 levels in the IMW group were significantly lower than those observed in the other two groups (p=0.019; p=0.035). Following the IMW intervention, the muscle oxygen saturation levels of the biceps brachii and vastus medialis in the subjects were significantly higher than those observed prior to training (p \u3c 0.05). However, no significant difference was noted between the two tests in the control group, indicating that the IMW intervention can mitigate the decline in muscle oxygenation during training, alleviate discomfort in the lungs and motor muscles, and regulate subjective load. The implementation of IMW intervention has the potential to mitigate the subjective burden experienced by athletes, thereby reducing discomfort during training. The combination of PFC activation level and muscle oxygen saturation index provides an explanation of the results. Nevertheless, research is required to ascertain whether IMW can have a sustained positive impact