Accurate Recognition of Volleyball Motion Based on Fusion of MEMS Inertial Measurement Unit and Video Analytic

Kaiqiao Peng, Yuliang Zhao*, Xiaopeng Sha, Wenqian Ma, Yufan Wang, Wen J. Li*

*Corresponding author for this work

    Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

    6 Citations (Scopus)

    Abstract

    This paper presents an motion recognition and analysis method based on the fusion of MEMS Inertial Measurement Unit (IMU)data and the recorded video for the volleyball skill assessment. Based on the synchronous video and acceleration data of the player's wrist, the complete spiking motion are carefully studied. Six kinds of the important characteristics were used to recognize the 12 trails of spiking motion of the same player by neural network. The results demonstrated that this method can recognize the motions of the same players with an accuracy of 89.6%. Furthermore, this proposed method can be extended to analyze the key motions of other types of sports, e.g., basketball, badminton, and baseball.
    Original languageEnglish
    Title of host publicationProceedings of 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems
    PublisherIEEE
    Pages440-444
    ISBN (Electronic)978-1-5386-7057-6
    DOIs
    Publication statusPublished - Jul 2018
    Event8th Annual IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, IEEE-CYBER 2018 - Tianjin, China
    Duration: 19 Jul 201823 Jul 2018

    Publication series

    Name8th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER

    Conference

    Conference8th Annual IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, IEEE-CYBER 2018
    PlaceChina
    CityTianjin
    Period19/07/1823/07/18

    Research Keywords

    • Inertial Measurement Unit
    • Motion Recognition
    • Neural Network
    • Spiking motion

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