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VolleyJump 2.0: A Computational Approach to Analysis and Classification of Volleyball Jumps Based on Multiple Inertial Devices

Published:30 November 2020Publication History

ABSTRACT

Technological resources have become indispensable in professional sports activities due to the critical level of competitiveness of athletes required in tournaments. One of the main aspects that characterize a volleyball athlete's success is his performance in the jumps performed during a match. This physical action is performed several times by the athlete to attack or prevent the success of an opposing team's attack. Wearable computing has become common in sports training, just as there are approaches that use sensors attached to the athletes' body to study and quantify the jump's movement. However, these marketable portable devices tend to be used as activity trackers that provide simple recommendations, such as the number of steps, heart rate, distance traveled, and amount of calories lost. Besides, real-time monitoring and evaluation solutions to provide personalized feedback and decisions are expensive and not viable for athletes without sponsorship. For this reason, this work presents VolleyJump 2.0, a computational approach to classify volleyball athletes jumps using techniques of extraction and selection of features from digital signals provided by inertial sensors. The performance results of the data set are also presented, in a promising way, in some machine learning models developed to predict volleyball jumps. Recent studies dealing with this topic show that classification models have good results for distinguishing jumps using a device that contains an accelerometer and gyroscope sensor. In this study, we proposed using prototypes of electronic devices of inertial sensors attached to the wrist and waist of the user. These sensors emit digital signals via WiFi connection, which will be processed to extract features and, from there, create a data-set of jump activity. With this, it will be possible to analyze the selection of characteristics and measure the performance of these variables using some classification models.

References

  1. Ciro Alminni, Gaetano Altavilla, Raffaele Scurati, and Francesca D'Elia. 2019. Effects induced through the use of physical and motor tests in volleyball. Journal of Human Sport and Exercise. https://doi.org/10.14198/jhse.2019.14.Proc4.20Google ScholarGoogle Scholar
  2. Renan Bandeira, Fernando Trinta, João Gomes, Marcio Maia, and Alexandre Araripe. 2018. VolleyJump: Uma aplicação para a análise de saltos no voleibol de praia. In ANAIS ESTENDIDOS DO XXIV SIMPÓSIO BRASILEIRO DE SISTEMAS MULTI-MÍDIA E WEB (WEBMEDIA) (Salvador). SBC, Porto Alegre, RS, Brasil, 115--119. https://doi.org/10.5753/webmedia.2018.4579Google ScholarGoogle Scholar
  3. Erich Gamma, Richard Helm, Ralph Johnson, and John M. Vlissides. 1994. Design Patterns: Elements of Reusable Object-Oriented Software (1 ed.). Addison-Wesley Professional. http://www.amazon.com/Design-Patterns-Elements-Reusable-Object-Oriented/dp/0201633612/ref=ntt_at_ep_dpi_1Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Florian Gottwalt, Elizabeth Chang, and Tharam Dillon. 2019. CorrCorr: A feature selection method for multivariate correlation network anomaly detection techniques. Computers and Security 83 (2019), 234--245. https://doi.org/10.1016/j.cose.2019.02.008Google ScholarGoogle ScholarCross RefCross Ref
  5. A. Holatka, H. Suwa, and K. Yasumoto. 2019. Volleyball Setting Technique Assessment Using a Single Point Sensor. In 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). 567--572. https://doi.org/10.1109/PERCOMW.2019.8730811Google ScholarGoogle ScholarCross RefCross Ref
  6. InvenSense Inc 2013. MPU-6000 and MPU-6050 Product Specification. InvenSense Inc. Rev. 3.4.Google ScholarGoogle Scholar
  7. N. M. Junior. 2015. Fundamentos que Fazem Ponto Durante o Jogo de Voleibol: um Estudo de Correlação. Revista Observatorio del Deporte (2015), 134--145.Google ScholarGoogle Scholar
  8. Javier Vales-Alonso, David Chaves-Díeguez, Pablo López-Matencio, Juan J. Alcaraz, Francisco J. Parrado-García, and Francisco Javier González-Castaño. 2015. SAETA: A Smart Coaching Assistant for Professional Volleyball Training. IEEE Trans. Systems, Man, and Cybernetics: Systems 45, 8 (2015), 1138--1150. https://doi.org/10.1109/TSMC.2015.2391258Google ScholarGoogle Scholar
  9. Y. Wang, Y. Zhao, R. H. M. Chan, and W.J. Li. 2018. Volleyball Skill Assessment Using a Single Wearable Micro Inertial Measurement Unit at Wrist. IEEE Access 6 (2018), 13758--13765.Google ScholarGoogle ScholarCross RefCross Ref

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  1. VolleyJump 2.0: A Computational Approach to Analysis and Classification of Volleyball Jumps Based on Multiple Inertial Devices

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        cover image ACM Conferences
        WebMedia '20: Proceedings of the Brazilian Symposium on Multimedia and the Web
        November 2020
        364 pages
        ISBN:9781450381963
        DOI:10.1145/3428658

        Copyright © 2020 ACM

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        Publication History

        • Published: 30 November 2020

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