Tennis singles match play induces inspiratory muscle fatigue in female tennis players

Osakabe, Jumpei; Ohya, Toshiyuki; Koizumi, Jun; Inada, Ryosuke; Matsumoto, Takaaki; Umemura, Yoshihisa

doi:10.7600/jpfsm.10.33

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…In the information age, huge amounts of data are generated every day. With the ever-increasing scale and dimension of data, how to effectively mine valuable information from these data has become a hot issue [22]. Machine learning algorithms are generally divided into supervised learning and unsupervised learning, and the DBSCAN clustering algorithm belongs to unsupervised machine learning [23].…”

Section: Dbscan Clustering Algorithmmentioning

confidence: 99%

Artificial Intelligence Technology to Record the Number of Times the Ball Passes the Net in Tennis Matches

Liu

Huang

2022

Wireless Communications and Mobile Computing

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Tennis competition is one of sports events. The vigorous development of sports can promote people’s pursuit of sports spirit and promote social unity and stability. However, in the course of social modernization development, sports events also need to follow closely, and we can no longer rely on traditional tools to support the development of events, which is not conducive to the positive development of sports. Due to the problem that the number of times the ball crosses the net in the traditional tennis game is too backward and the error rate is high, this paper uses artificial intelligence technology to record the number of times the ball crosses the net in tennis games and introduces the Center Net target based on the diagnostic criteria and comprehensive evaluation of tennis nets. Detect and track tennis balls by target recognition, feature extraction, and other methods, and record the times of net passing. However, considering that abnormal behaviors will also occur in the process of tennis passing through the net, the density-based DBSCAN clustering algorithm is used to discriminate and record abnormal behaviors in tennis matches. In order to verify the detection performance of DBSCAN clustering algorithm and Center Net target detection, the video of tennis match was analyzed and compared by drone from the perspective of time and space. By recording the number of times the ball crosses the net during the tennis receiving, connecting and attacking, and stalemate phases, the performance of the two recording times is compared. R 2 are all higher than 0.94, and R values are 0.982 and 0.963, respectively, so the recall rate of DBSCAN clustering algorithm is 0.02 higher, which is better than Center Net target detection. Using artificial intelligence technology to record the number of times the ball crosses the net in tennis games can not only improve the scientificity and accuracy of the record but also promote the development of the sports industry.

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Section: Dbscan Clustering Algorithmmentioning

confidence: 99%