摘要：
To overcome the problems of low recognition accuracy, poor recognition recall, and long recognition time in traditional badminton video action recognition methods, a badminton video action recognition method based on an adaptive enhanced AdaBoost algorithm is proposed. Firstly, the badminton video actions are collected through inertial sensors, and the badminton action videos are captured to construct an action dataset. The data in this dataset is normalised, and then the badminton video action features are extracted. The weighted fusion method is used to fuse the extracted badminton video action features. Finally, the fused action features are used as the basis, Construct a badminton video action classifier using the adaptive enhanced AdaBoost algorithm, and output the badminton video action recognition results through the classifier. The experimental results show that the proposed method has good performance in recognising badminton video actions.
作者机构：
["Yan, Ying"] College of Physical Education, Hunan City University, Yiyang, 413000, Hunan, China. yanying@hncu.edu.cn;["Yan, Ying"] School of Educational Studies, Universiti Sains Malaysia, Gelugor, Penang, 11800, Malaysia. yanying@hncu.edu.cn
摘要：
Aiming at the optimization of public sports service quality, this study analyzes the public sports service data deeply by constructing a supervised learning model. Firstly, the theoretical framework of this study is established. Secondly, the technical framework is constructed based on the supervised learning model. Finally, the comprehensive performance of the model is evaluated using a dataset and practical application. The results show that when the model is used to process public sports service data, its performance is excellent. Specifically, the model's accuracy and recall in processing various types of data markedly exceed expectations, with the accuracy reaching more than 88% and the recall remaining at a similarly high level. This remarkable result not only validates the supervised learning model's practicability in the quality optimization of public sports services but also highlights its huge application potential and value. In addition, the possibility and challenge of the model in practical application are also discussed, which provides a useful reference for further improving the quality of public sports service. The findings of this study enrich the research methods in the field of public sports services and offer a scientific basis for relevant decision-making, which helps promote the continuous optimization and development of public sports services.
摘要：
This project aims to improve the accuracy of fitness and physical pressure ratings, focusing on basketball, by integrating artificial intelligence (AI) into data collection and training. Athletes and fitness fanatics can benefit greatly from the data collected using complex AI algorithms to determine stress levels. This study employs the Intelligent Physiological Monitoring Framework for Exercise and Fitness Pressure Measurement (IPM-EFPM) to perform automated stress tests that employ AI to enhance the precision of exercise and fitness pressure measurements. Basketball training programs can benefit from this framework's utilization of state-of-the-art technology, meticulous monitoring of exercise-induced stress, and continuous validation and improvement. The IPM-EFPM system gathers data from wearable sensors, uses real-time location systems, and employs artificial intelligence's Long Short-Term Memory (LSTM) and machine learning algorithms to uncover new insights in healthcare and sports. To accurately record fitness strain, physical activity, exercise-induced stress, and sports like basketball, this system employs cutting-edge artificial intelligence technologies, such as wearable sensors and current gathering data methods. Placement of sensors, real-time data collecting, data preprocessing and integrating, evaluation of stress by artificial intelligence algorithms, discovery and application of new information, validation and improvement are all parts of an iterative method that has been fine-tuned for use in sports and fitness settings by the IPM-EFPM. Examining the intricate relationship between AI, physical activity, and psychological stress is the main objective of this research. This could have real-world uses tailored to the sports world, particularly for basketball players.
This project aims to improve the accuracy of fitness and physical pressure ratings, focusing on basketball, by integrating artificial intelligence (AI) into data collection and training. Athletes and fitness fanatics can benefit greatly from the data collected using complex AI algorithms to determine stress levels. This study employs the Intelligent Physiological Monitoring Framework for Exercise and Fitness Pressure Measurement (IPM-EFPM) to perform automated stress tests that employ AI to enhance the precision of exercise and fitness pressure measurements. Basketball training programs can benefit from this framework's utilization of state-of-the-art technology, meticulous monitoring of exercise-induced stress, and continuous validation and improvement. The IPM-EFPM system gathers data from wearable sensors, uses real-time location systems, and employs artificial intelligence's Long Short-Term Memory (LSTM) and machine learning algorithms to uncover new insights in healthcare and sports. To accurately record fitness strain, physical activity, exercise-induced stress, and sports like basketball, this system employs cutting-edge artificial intelligence technologies, such as wearable sensors and current gathering data methods. Placement of sensors, real-time data collecting, data preprocessing and integrating, evaluation of stress by artificial intelligence algorithms, discovery and application of new information, validation and improvement are all parts of an iterative method that has been fine-tuned for use in sports and fitness settings by the IPM-EFPM. Examining the intricate relationship between AI, physical activity, and psychological stress is the main objective of this research. This could have real-world uses tailored to the sports world, particularly for basketball players.
摘要：
BACKGROUND: Physical activity significantly influences health-related behaviors, encompassing physical and mental well-being. Physical activity has been linked to enhancing health behavior, mental health, and psychological resilience. The current study is based on participants who were active in physical activity to improve health and well-being. OBJECTIVES: To examine the influences of physical activity on health behavior, mental health, and psychological resilience, considering the moderating role of quality of life. METHOD: A thorough cross-sectional online survey was conducted from April 15, 2023, to October 15, 2023. The survey was comprehensive and lasted for six months. The online poll received more than one thousand responses under convenience sampling. The selection criteria for the study were above 21 years old, and participants were active in physical activity to improve health and well-being. The collected data were analyzed using appropriate statistical SPSS-25 and SmartPLS 4.0 software to investigate the proposed research paradigm. RESULTS: SEM results of model 1 (direct coefficients) show that PA has a positive effect on HeB, MeH, PsR, HeB on MeH, HeB on PsR. Out of six (in model 2), four moderating effects of QOL were significant, and two were statistically insignificant. CONCLUSION: It has been observed that the quality of life has a moderating role in the relationships between physical exercise and several aspects, such as psychological resilience, mental health, and health-related behavior. It is imperative to emphasize the importance of fostering consistent engagement in physical activity to cultivate a well-balanced and health-conscious way of life.
摘要：
This study conducted to examine the influence of sports events on tourists destination choice behaviour in China. The questionnaire was distributed both online and offline in the area surrounding City-A. The author through personal networks, including family and friends, as well as via WeChat group emails primarily disseminated online questionnaires. The findings reveal that 57.8% of respondents expressed a greater likelihood of attending large sports events, while 59.9% indicated an increased intention to travel to City-A in the future. The occurrence of large-scale sports events has played a significant role in establishing City A's brand and has contributed to fostering a sports-oriented cultural environment, which is appealing to tourists. The study also highlights that the decision-making process regarding tourists' destination choices influenced by the presence of large-scale sports events, affecting tourists behavioural patterns in selecting destinations.
