Table Tennis Stroke Recognition Based on Body Sensor Network

  • Ruichen LiuEmail author
  • Zhelong Wang
  • Xin Shi
  • Hongyu Zhao
  • Sen Qiu
  • Jie Li
  • Ning Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11874)


Table tennis stroke recognition is very important for athletes to analyze their sports skills. It can help players to regulate hitting movement and calculate sports consumption. Different players have different stroke motions, which makes stroke recognition more difficult. In order to accurately distinguish the stroke movement, this paper uses body sensor networks (BSN) to collect motion data. Sensors collecting acceleration and angular velocity information are placed on the upper arm, lower arm and back respectively. Principal component analysis (PCA) is employed to reduce the feature dimensions and support vector machine (SVM) is used to recognize strokes. Compared with other classification algorithms, the final experimental results (97.41% accuracy) illustrate that the algorithm proposed in the paper is effective and useful.


Motion recognition Micro-electromechanical systems Principal component analysis Support vector machine 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ruichen Liu
    • 1
    Email author
  • Zhelong Wang
    • 1
  • Xin Shi
    • 1
  • Hongyu Zhao
    • 1
  • Sen Qiu
    • 1
  • Jie Li
    • 1
  • Ning Yang
    • 1
  1. 1.School of Control Science and EngineeringDalian University of TechnologyDalianChina

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