scholarly journals Optimization Analysis of Tennis Players’ Physical Fitness Index Based on Data Mining and Mobile Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shoudong Zhang ◽  
Huaqing Mao
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Physical Fitness ◽  
Physical Condition ◽  
Performance Model ◽  
Short Term ◽  
Tennis Players ◽  
Good Physical Condition ◽  
Processor Performance ◽  
Routine Monitoring

Tennis is a very explosive, continuous, and intense sport, including many continuous short-term explosive actions. It has the characteristics of short-term, high-intensity, high-density training, and it belongs to the category of purely competitive skills. In the competition, athletes must maintain good physical condition, physical fitness, and long-term endurance in order to demonstrate outstanding technical and tactical skills. Therefore, this paper proposes a mobile processor performance data mining framework MobilePerfMiner, which uses hardware counters and iteratively uses the XGBoost algorithm to build a performance model, ranks the importance of the microarchitecture events of the big data task, and reduces the performance big data dimension, so as to optimize the big data algorithm according to the performance characteristics described. Undoubtedly, the comprehensive monitoring of the sports training process is complex system engineering. The main monitoring includes three aspects: physical condition, technical and tactical skills, and intelligence. Sports technology is reflected in the ultimate load. According to the convenience and actual needs of the research, this article will discuss the methods of evaluating tennis training load and the actual technical and tactical parameter characteristics that can be obtained by studying the characteristics of tennis, namely, kinematics. Parameters for noncontact testing, the next step is to discuss the appropriateness and necessity of the load, as well as the technical and routine monitoring of tennis training ability. The final experimental results show that it can improve the physical energy of tennis players by more than 17%.

scholarly journals Influence of Healthy Physical Fitness on the Teaching of Aerobics Course Based on Big Data Mining Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Laipeng Xiao
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Physical Fitness ◽  
Physical Training ◽  
Body Shape ◽  
Teaching Experiment ◽  
The Body ◽  
Mining Technology ◽  
Big Data Mining ◽  
The Impact

Healthy physical fitness is one of the hot topics discussed by scholars at home and abroad in recent years, and it is a key indicator for evaluating students’ physical function and body shape. Aerobics, also known as bodybuilding, means that the body and health of students should have a better promotion effect, but in reality, many students found that after elective aerobics, body shape and health level basically did not improve, which is related to the setting of aerobics courses, especially the lack of physical training. Aerobics and other sports have common requirements in physical training, such as strength quality, speed quality, endurance quality, agility quality, and flexibility quality. This article is aimed at studying the impact of healthy physical fitness based on big data mining technology on the teaching of aerobics. On the basis of analyzing the process of data mining, the composition of healthy physical fitness, and the role of aerobics, it is used to test students in a certain university through experimental methods and statistical methods. Carry out aerobics teaching experiment, and compare and analyze the data measured by the experimental samples. The experimental results show that the use of healthy physical fitness in aerobics teaching can effectively promote the learning and improvement of aerobics skills.

Growth, Maturation and Exercise During Youth—2016

2017 ◽  
Vol 29 (1) ◽  
pp. 3-7 ◽  
Author(s):  
Jaak Jürimäe
Keyword(s):  
Physical Activity ◽  
Physical Fitness ◽  
Short Term ◽  
Tennis Players ◽  
Positive Effects ◽  
Junior Tennis ◽  
And Performance ◽  
Time Varying Covariates ◽  
Methodological Paper ◽  
Selection Of

For The Year that Was—2016, I have selected three papers in the area of growth, maturation, and exercise during youth. The year of 2016 was a successful year and it was not an easy task to highlight the most significant publications in this specific area of pediatric exercise science. The first paper has been chosen because it provides, for the first time, the growth and maturational status of elite junior tennis players belonging to the top eight players in the National rankings in various ages and compares against population norms. It appeared that individual differences in growth and maturation contribute towards the selection of elite junior tennis players in both sexes, with a bias towards these athletes who are comparatively tall and heavy for their age already in younger ages. The second paper is a methodological paper and was selected because it provides a unique perspective on the use of different tracking coefficients to investigate short-term tracking of cardiorespiratory and performance-related physical fitness among adolescents during growth and maturation. Specifically, three distinct statistical approaches were applied in this paper: auto-correlations, mulitilevel modeling corrected tracking values for time-varying covariates and Cohen`s Kappa in order to identify group and individual tracking as well as individuals whose trajectories are unstable across time. This methodological paper demonstrated the importance of the selection of the statistical approach to monitor and describe short-term tracking of cardiorespiratory and performance-related physical fitness variables in adolescents during growth and maturation. The third selected paper provided some evidence that the consequence of physical activity during childhood can be far reaching as physical activity might not only promote health benefits but also have positive effects on adulthood earnings.

scholarly journals Coordination Exercise for 10 Year Old Beginner Tennis Players

2020 ◽  
Vol 5 (4) ◽  
pp. 466
Author(s):  
Gumilar Mulya ◽  
Resty Agustryani
Keyword(s):  
Elementary School ◽  
Physical Condition ◽  
Sampling Technique ◽  
School Students ◽  
Tennis Players ◽  
Elementary School Age ◽  
Good Physical Condition ◽  
Coordination Training ◽  
Hand Coordination ◽  
Tennis Court

<strong>Abstract:</strong> There are many forms of training to improve the quality of physical condition of the tennis court athletes, one of which is coordination training. Thus, the author would like to try applying various forms of coordination exercises that are applied to children aged 10-11 years who participate in tennis clubs in the City of Tasikmalaya to support hand coordination. The aim of this research is to find out eye-hand coordination in elementary school students. The focus of this research is limited to the effectiveness of Coordination to support good physical condition in children. This research used experimental research methods. The population in this research were all 30 children who joined the tennis club in Tasikmalaya City. The sampling technique used is the total sampling technique. In order to get objective data, the author uses a measuring instrument in the form of a test that is eye-hand coordination test. Based on the results of this research and statistical calculations on the data obtained in this research, the authors can conclude that coordination exercises have a significant influence on improving eye-hand coordination especially for beginner tennis players of elementary school age.

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)

2019 ◽  
Author(s):  
Meghana Bastwadkar ◽  
Carolyn McGregor ◽  
S Balaji
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Intensive Care ◽  
Literature Review ◽  
Health Monitoring ◽  
Data Analytics ◽  
Neonatal Intensive Care ◽  
Big Data Analytics ◽  
Healthcare Facilities

BACKGROUND This paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. OBJECTIVE The aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics METHODS Literature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. RESULTS In total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great diversity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. CONCLUSIONS The current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.

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