A Visualization Template for the Graphical Representation of Sport Injury Antecedents and Consequences Models and Data

Brewer, Britton W.; Brewer, Travis P. Van

doi:10.3390/jfmk5040087

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…e huge user scale brings a huge amount of information and data, which contains great value. At present, sports health applications [7][8][9][10] are more focused on data collection [11] and data visualization display [12][13][14] and lack further analysis and mining of user health data. Reasonable advice and help is the main direction of sports and health applications in the intelligent age.…”

Section: Introductionmentioning

confidence: 99%

Sports Training Health Analysis Algorithm Based on Heart Rhythm Feature Extraction and Convolutional Neural Network

Xiao³

2021

Journal of Healthcare Engineering

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Intelligent sports equipment and software have emerged in the field of sports as a result of the advancement of information technology, allowing professional athletes to collect and visually display the movement and physical signs of the human body to aid in the planning of sports strategies. Intuitive data, on the other hand, cannot assist ordinary people who lack professional knowledge in exercising correctly. As a result, in the field of intelligent sports and health, effective use of collected exercise and physical sign data to analyze the user’s personal physical condition and generate reasonable exercise suggestions has emerged as a research direction. In humans, the heart sound signal is a biological signal. It can help people detect and monitor heart health problems by analyzing the characteristics of heart sound signals. The goal of this paper is to use heart sound to identify and analyze athletes’ training health. It provides a revolutionary health analysis algorithm based on heart rhythm feature extraction and convolutional neural networks, which is based on exercise training. It greatly improves the accuracy of the recognition and prediction of the athlete’s training health status.

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Section: Introductionmentioning

confidence: 99%

Sports Training Health Analysis Algorithm Based on Heart Rhythm Feature Extraction and Convolutional Neural Network

Xiao³

2021

Journal of Healthcare Engineering

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“…Sports contain players' physical activity, and behavioral actions between athletes allow time, description, and count data on action to be recorded [2]. The advent of competitive data, has therefore, given fuel to research in competitive sports and offers a platform to investigate the law of human life and human inclinations [3]. The big data age has had an unparalleled influence on sport's development [4].…”

Section: Introduction To Sports Data Visualizationmentioning

confidence: 99%

Hybrid design for sports data visualization using AI and big data analytics

Liu

Mahapatra

Mayuri

2021

Complex Intell. Syst.

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In sports data analysis and visualization, understanding collective tactical behavior has become an integral part. Interactive and automatic data analysis is instrumental in making use of growing amounts of compound information. In professional team sports, gathering and analyzing sportsperson monitoring data are common practice, intending to evaluate fatigue and succeeding adaptation responses, analyze performance potential, and reduce injury and illness risk. Data visualization technology born in the era of big data analytics provides a good foundation for further developing fitness tools based on artificial intelligence (AI). Hence, this study proposed a video-based effective visualization framework (VEVF) based on artificial intelligence and big data analytics. This study uses the machine learning method to categorize the sports video by extracting both the videos' temporal and spatial features. Our system is based on convolutional neural networks united with temporal pooling layers. The experimental outcomes demonstrate that the recommended VEVF model enhances the accuracy ratio of 98.7%, recall ratio of 94.5%, F1-score ratio of 97.9%, the precision ratio of 96.7%, the error rate of 29.1%, the performance ratio of 95.2%, an efficiency ratio of 96.1% compared to other existing models.

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Big data analytics for image processing and computer vision technologies in sports health management

Jin,

Zhan

2024

THC

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BACKGROUND: Visualization of sports has a lot of potential for future development in data sports because of how quickly things are changing and how much sports depend on data. Presently, conventional systems fail to accurately address sports persons’ dynamic health data change with less error rate. Further, those systems are unable to distinguish players’ health data and their visualization in a precise manner. An excellent starting point for building fitness solutions based on computer vision technology is the data visualization technology that arose in the age of big data analytics. OBJECTIVE: This research presents a Big Data Analytic assisted Computer Vision Model (BD-CVM) for effective sports persons healthcare data management with improved accuracy and precision. METHODS: The fitness and health of professional athletes are analyzed using information from a publicly available sports visualization dataset. Machine learning-assisted computer vision dynamic algorithm has been used for an effective image featuring and classification by categorizing sports videos through temporal and geographical data. RESULTS: The significance of big data’s great potential in screening data during a sporting event can be reasonably analyzed and processed effectively with less error rate. The proposed BD-CVM utilized an error analysis module which can be embedded in the design further to ensure the accuracy requirements in the data processing from sports videos. CONCLUSION: The research findings of this paper demonstrate that the strategy presented here can potentially improve accuracy and precision and optimize mean square error in sports data classification and visualization.

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A Visualization Template for the Graphical Representation of Sport Injury Antecedents and Consequences Models and Data

Cited by 3 publications

References 27 publications

Sports Training Health Analysis Algorithm Based on Heart Rhythm Feature Extraction and Convolutional Neural Network

Sports Training Health Analysis Algorithm Based on Heart Rhythm Feature Extraction and Convolutional Neural Network

Hybrid design for sports data visualization using AI and big data analytics

Big data analytics for image processing and computer vision technologies in sports health management

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