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Improvement of table tennis technology based on data mining in the environment of wireless sensor networks

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  • Hongjian Ma

Abstract

With the development of Internet of things technology, the combination of Internet of things technology and sports competition parameter collection technology, so as to carry out rapid and accurate retrieval and positioning of technology and tactics, has innovation in the current research field. In the high-level table tennis competition, the use of technology and tactics is closely related to the gain and loss of points. At present, the traditional table tennis video mining algorithm has some problems such as low efficiency and poor performance of optimization classification. Based on this, this article introduces the big data platform of the wireless sensor networks to construct the table tennis match database, realizing the real-time updating of table tennis match parameters and the call of historical data at any time. Then establishing a data mining model to realize the data and dynamic analysis of table tennis matches. Finally, based on this strategic analysis system, the data collected from two table tennis competitions are simulated, and the tactical recommendation of theoretical analysis is obtained, which provides a theoretical basis for the digitization of table tennis sports.

Suggested Citation

  • Hongjian Ma, 2020. "Improvement of table tennis technology based on data mining in the environment of wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 16(10), pages 15501477209, October.
    • Handle: RePEc:sae:intdis:v:16:y:2020:i:10:p:1550147720961343
    DOI: 10.1177/1550147720961343
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    1. Vaishali Mirge & Kesari Verma & Shubhrata Gupta, 2017. "Dense traffic flow patterns mining in bi-directional road networks using density based trajectory clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 547-561, September.
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