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Modern technology, artificial intelligence, machine learning and internet of things based revolution in sports by employing graph theory matrix approach

Authors :
Lingtao Wen
Zebo Qiao
Jun Mo
Source :
AIMS Mathematics, Vol 9, Iss 1, Pp 1211-1226 (2024)
Publication Year :
2024
Publisher :
AIMS Press, 2024.

Abstract

The sports industry is gaining popularity with time and all the countries are investing a lot of money for fame and entertainment around the world. To ensure the high quality of sports, modern techniques such as machine learning (ML), artificial intelligence (AI) and the Internet of Things (IoT) are playing a very optimistic role. Various IoT-grounded smart sensors are implemented with integration in AI and ML for the safety and high performance of the players. Based on the numerous applications of modern technologies, it is very convenient to capture different body movements of the players and avoid any severe injuries and long-term health issues. AI and IoT-driven smart devices are revolutionizing the analysis of athletes' training and performance, offering precise insights for their improvement. This article delved into the remarkable strides made in scientific sports, highlighting how computer-based elements are reshaping the sports landscape for athletes and spectators alike. These innovations enable real-time health monitoring, prevent accidents, capture diverse postures and analyze sporting outcomes. By extensively reviewing existing literature, key features have been identified and prioritized. Using the graph theory matrix approach (GTMA), this piece compared and ranks available alternatives based on these selected features. Moreover, the parameter matrix and normalized matrix were reported in tabulated form and the ranks for ten paradigms are illustrated graphically for better visualization.

Details

Language :
English
ISSN :
24736988
Volume :
9
Issue :
1
Database :
Directory of Open Access Journals
Journal :
AIMS Mathematics
Publication Type :
Academic Journal
Accession number :
edsdoj.81d2d64c8dd94e0986fa8bf877865afd
Document Type :
article
Full Text :
https://doi.org/10.3934/math.2024060?viewType=HTML