Research article Special Issues

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

  • Received: 16 October 2023 Revised: 21 November 2023 Accepted: 24 November 2023 Published: 07 December 2023
  • MSC : Researcharticle

  • 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.

    Citation: Lingtao Wen, Zebo Qiao, Jun Mo. Modern technology, artificial intelligence, machine learning and internet of things based revolution in sports by employing graph theory matrix approach[J]. AIMS Mathematics, 2024, 9(1): 1211-1226. doi: 10.3934/math.2024060

    Related Papers:

  • 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.



    加载中


    [1] H. V. Eetvelde, L. D. Mendonça, C. Ley, R. Seil, T. Tischer, Machine learning methods in sport injury prediction and prevention: a systematic review, J. Exp. Orthop., 8 (2021), 27. https://doi.org/10.1186/s40634-021-00346-x doi: 10.1186/s40634-021-00346-x
    [2] P. S. H. V. Goud, Y. M. Roopa, B. Padmaja, Player performance analysis in sports: with fusion of machine learning and wearable technology, 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2019,600–603. https://doi.org/10.1109/ICCMC.2019.8819815
    [3] G. Li, Research on sports simulation and fatigue characteristics of athletes based on machine learning, J. Intell. Fuzzy Syst., 40, (2021), 7531–7542. https://doi.org/10.3233/JIFS-189574 doi: 10.3233/JIFS-189574
    [4] B. Velichkov, I. Koychev, S. Boytcheva, Deep learning contextual models for prediction of sport event outcome from sportsman's interviews, International Conference on Recent Advances in Natural Language Processing, Varna, Bulgaria, 2019, 1240–1246. https://doi.org/10.26615/978-954-452-056-4_142
    [5] K. Takano, K. F. Li, A multimedia tennis instruction system: Tracking and classifying swing motions, Int. J. Space Based Situated Comput., 3 (2013), 155–168. https://doi.org/10.1504/IJSSC.2013.056406 doi: 10.1504/IJSSC.2013.056406
    [6] R. Bartlett, Artificial intelligence in sports biomechanics: new dawn or false hope, J. Sports Sci. Med., 5 (2006), 474–479.
    [7] M. Oytun, C. Tinazci, B. Sekeroglu, C. Acikada, H. U. Yavuz, Performance prediction and evaluation in female handball players using machine learning models, IEEE Access, 8 (2020), 16321–116335. https://doi.org/10.1109/ACCESS.2020.3004182 doi: 10.1109/ACCESS.2020.3004182
    [8] H. Novatchkov, A. Baca, Artificial intelligence in sports on the example of weight training, J. Sports Sci. Med., 12 (2013), 27–37.
    [9] A. Farrokhi, R. Farahbakhsh, J. Rezazadeh, R. Minerva, Application of Internet of Things and artificial intelligence for smart fitness: a survey, Comput. Netw., 189 (2021), 107859. https://doi.org/10.1016/j.comnet.2021.107859 doi: 10.1016/j.comnet.2021.107859
    [10] S. V. Mukhaev, L. A. Semenov, Conversion of science-intensive sports technologies as sports training system modernization tool, Teoriya I Praktika Fizicheskoy Kultury, 2021 (2021), 6–8.
    [11] B. Zhang, M. Lyu, L. Zhang, Y. Wu, Artificial intelligence-based joint movement estimation method for football players in sports training, Mob. Inf. Syst., 2021 (2021), 9956482. https://doi.org/10.1155/2021/9956482 doi: 10.1155/2021/9956482
    [12] F. Tan, X. Xie, Recognition Technology of athlete's limb movement combined based on the integrated learning algorithm, J. Sensors, 2021 (2021), 3057557. https://doi.org/10.1155/2021/3057557 doi: 10.1155/2021/3057557
    [13] T. Tang, M. Hyun-Joo, Research on sports dance movement detection based on pose recognition, Math. Probl. Eng., 2022 (2022), 4755127, https://doi.org/10.1155/2022/4755127 doi: 10.1155/2022/4755127
    [14] H. Su, Z. Su, Y. Xia, The effect of physical training of athletes based on parametric bayesian estimation in the context of big data, Math. Probl. Eng., 2022 (2022), 2089446. https://doi.org/10.1155/2022/2089446 doi: 10.1155/2022/2089446
    [15] S. Liu, "IoT Plus" and intelligent sports system under the background of artificial intelligence: take swimming as an example, In: Big data analytics for cyber-physical system in smart city, Singapore: Springer, 2020,195–201. https://doi.org/10.1007/978-981-33-4572-0_28
    [16] W. Zhou, Z. Fu, Adoption of bio-image technology on rehabilitation intervention of sports injury of golf, J. Supercomput., 77 (2021), 11310–11327. https://doi.org/10.1007/s11227-021-03732-5 doi: 10.1007/s11227-021-03732-5
    [17] M. A. Ikram, M. D. Alshehri, F. K. Hussain, Architecture of an IoT-based system for football supervision (IoT Football), 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), Milan, Italy, 2015, 69–74. https://doi.org/10.1109/WF-IoT.2015.7389029
    [18] J. Kim, S. Kim, Development of wearable sports helmet model using IoT server technology, In: Information science and applications, Singapore: Springer, 2019,691–695. https://doi.org/10.1007/978-981-15-1465-4_69
    [19] D. Helbing, D. Brockmann, T. Chadefaux, K. Donnay, U. Blanke, O. Woolley-Meza, et al., Saving Human lives: What complexity science and information systems can contribute, J. Stat. Phys., 158 (2015), 735–781. https://doi.org/10.1007/s10955-014-1024-9 doi: 10.1007/s10955-014-1024-9
    [20] M. Perc, M. Ozer, J. Hojnik, Social and juristic challenges of artificial intelligence, Palgrave Commun., 5 (2019), 61. https://doi.org/10.1057/s41599-019-0278-x doi: 10.1057/s41599-019-0278-x
    [21] N. A. Kofahi, R. M. Al-Khatib, A. Alomari, T. A. Mansi, A smart real-time IoT-based system for monitoring health of athletes, Int. J. Com. Dig. Sys., 12 (2021), 141–148. https://doi.org/10.12785/ijcds/120113 doi: 10.12785/ijcds/120113
    [22] N. Mozaffari, J. Rezazadeh, R. Farahbakhsh, S. Yazdani, K. Sandrasegaran, Practical fall detection based on IoT technologies: a survey, Internet of Things, 8 (2019), 100124. https://doi.org/10.1016/j.iot.2019.100124 doi: 10.1016/j.iot.2019.100124
    [23] A. M. Kabilan, K. Agathiyan, G. V. S. Lohit, Early detection of foot pressure monitoring for sports person using IoT, In: Advances in machine learning and computational intelligence, Singapore: Springer, 2021,587–594. https://doi.org/10.1007/978-981-15-5243-4_54
    [24] N. Li, X. Y. Zhu, Design and application of blockchain and IoT-enabled sports injury rehabilitation monitoring system using neural network, Soft Comput., 27 (2023), 11815–11832, https://doi.org/10.1007/s00500-023-08677-w doi: 10.1007/s00500-023-08677-w
    [25] B. Jie, Sports injury degree evaluation model based on complex network model, Modern Electronics Technique, 41 (2018), 165–168. https://doi.org/10.16652/j.issn.1004-373x.2018.06.040 doi: 10.16652/j.issn.1004-373x.2018.06.040
    [26] N. K. Geetha, P. Sekar, Graph theory matrix approach–A qualitative decision making tool, Materials Today: Proceedings, 4 (2017), 7741–7749. https://doi.org/10.1016/j.matpr.2017.07.109 doi: 10.1016/j.matpr.2017.07.109
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1346) PDF downloads(89) Cited by(1)

Article outline

Figures and Tables

Figures(4)  /  Tables(6)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog