Construction of Intelligent Campus Tennis Players’ Body Data Monitoring and Injury Warning System Based on Data Fusion

Yan, Li

doi:10.1590/1517-8692202127022021_0018

Cited by 11 publications

(9 citation statements)

References 5 publications

(5 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e mean time distribution of the index of health and sports behavioral efficiency index of college students is calculated by formula (5).…”

Section: Fuzzy Decision Feature Extractionmentioning

confidence: 99%

“…ere are a large number of information data that can be mined in the university education system, such as teaching evaluation, student achievement, student information, and so on. However, at present, the research of data mining in higher education is still based on theory, and there are not many formed products really applied to data mining technology [5,6]. erefore, the college students' fitness analysis system uses in-depth analysis of student behavior and performance using data collected from student fitness tests and the "Physical Self-Assessment Sheet" based on data mining technology.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Construction Principles of Physical Fitness Training Objective System Based on Machine Learning and Data Mining

Wang

Sun

2022

Security and Communication Networks

View full text Add to dashboard Cite

The objective is to address the issue of simplification of physical education classes offered by large colleges and universities. The evaluation standard of physical education curriculum is not unified. The physical education management system focuses on the functions of collecting information, sorting, and statistics and has low timeliness and guiding significance. This paper puts forward an analysis of the construction principle of physical fitness training target system based on machine learning and data mining. This paper uses informational analysis to statistically analyze the healthy behavior of college students, to guide the physical education of college students, and to propose a model for the analysis of healthy sports behavior of college students based on data mining technology. Create a decision tree template for students whose cardiovascular function does not meet the standard using the decision tree algorithm. The association rule algorithm is used to mine the association of five indexes of physical health, so as to judge the hidden law between students’ physical fitness and behavior habits and get the correlation information of various physical health indicators. The simulation results show that, through the prediction of college students’ healthy sports behavior data, when the sample point is 5, the original value data is 16, which is higher than the estimated value, the convergence of the overall data characteristic distribution is good, and the disturbance error is low. Therefore, using this method to analyze the application of college students’ healthy sports behavior has a high accuracy of sports-related data mining and can effectively guide college students’ sports management and training.

show abstract

“…e mean time distribution of the index of health and sports behavioral efficiency index of college students is calculated by formula (5).…”

Section: Fuzzy Decision Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Construction Principles of Physical Fitness Training Objective System Based on Machine Learning and Data Mining

Wang

Sun

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Through the control system, the robot moves to the specified position and realizes the action of picking up the ball [8]. Ganser et al proposed an embedded intelligent ball-picking car, and the scheme uses color and shape recognition algorithm, combined with PID control algorithm, respectively, using motor drive car body movement and steering gear control car ball-picking action, to achieve the function of picking up tennis balls [9,10]. However, this scheme is only a theoretical solution, and the concrete realization remains to be practiced.…”

Section: Literature Reviewmentioning

confidence: 99%

Motion Control Analysis of Tennis Robot Based on Ant Colony Algorithm

et al. 2022

View full text Add to dashboard Cite

Visual recognition and automatic control technology is an important way to realize robot automatic ball picking. Therefore, a tennis robot motion control method based on ant colony algorithm is evaluated. After the camera position was fixed, the motion control system software was designed, and the optimal path was solved by ant colony algorithm. The results show that the two adjacent positioning errors and the current total positioning errors fluctuate around -0.05 mm and -2.29 mm, respectively, and the fluctuation range is less than 3.50 mm. Ant colony algorithm is superior to greedy algorithm in path planning of collecting tennis balls. The number of iterations of ant colony algorithm for optimal path planning of 30 to 50 tennis balls is about 20 to 30 times, and the path length is reduced to reduce the time of collecting tennis balls, which meets the actual work requirements.

show abstract

“…The study provided a systematic understanding of the causes of injury and disease in athletes and developed protective measures accordingly. Li used data fusion techniques to analyze and assess potential injury factors in various sports and based on this, developed a dynamic chain model for early warning of risk factors for sports injuries [9]. The study provides a reference for athletes to avoid and reduce injury risk and guarantee normal training and competition, and the authors also applied the research results to tennis training and achieved scientific results.…”

Section: Related Workmentioning

confidence: 99%

A Radial Basis Network-based Early Warning Algorithm for Physical Injuries in Marathon Athletes

Jiao¹,

Luo²

2023

IJACSA

View full text Add to dashboard Cite

For marathon runners, a single injury may affect their lifelong athletic career, so their injury management is very important. The current injury management for marathon runners has a certain lag, and the current injury warning is mainly based on manual teams, which is costly and poorly automated. To solve these problems, the study proposes a marathon athlete physical injury warning algorithm based on inertia weight adjustment optimized radial basis network. Particle swarm optimization technology has also been incorporated into early warning algorithms. Finally, an athlete injury and disease early warning model is constructed based on the algorithm. The results of performance tests show that the algorithm has a minimum fitness function value of 0.13, which is significantly lower than the current algorithm used for comparison. In the test with real data, the MAPE of the proposed algorithm was as low as 7.598% and the agreement of the hazard score results with the expert human assessment reached 100%. The results of the study indicate the practicality of the algorithm to assist work teams and perform early warning of physical injuries in athletes. However, the high number of iterations required is a limitation awaiting resolution.

show abstract

Construction of Intelligent Campus Tennis Players’ Body Data Monitoring and Injury Warning System Based on Data Fusion

Cited by 11 publications

References 5 publications

Construction Principles of Physical Fitness Training Objective System Based on Machine Learning and Data Mining

Construction Principles of Physical Fitness Training Objective System Based on Machine Learning and Data Mining

Motion Control Analysis of Tennis Robot Based on Ant Colony Algorithm

A Radial Basis Network-based Early Warning Algorithm for Physical Injuries in Marathon Athletes

Contact Info

Product

Resources

About