Advertisement

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)

Abstract

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.

Keywords

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

References

  1. 1.
    Mao, B.-J.: Different techniques comparison in biomechanical analysis of ping pong. Appl. Mech. Mater. 166–169, 3106–3109 (2012)CrossRefGoogle Scholar
  2. 2.
    Muelling, K., Boularias, A., Mohler, B., Scholkopf, B., Peters, J.: Learning strategies in table tennis using inverse reinforcement learning. Biol. Cybern. 108(5), 603–619 (2014)CrossRefGoogle Scholar
  3. 3.
    Wang, Z., et al.: Inertial sensor-based analysis of equestrian sports between beginner and professional riders under different horse gaits. IEEE Trans. Instrum. Meas. 67, 1–13 (2018)CrossRefGoogle Scholar
  4. 4.
    Chen, L., Hoey, J., Nugentt, C.D., Cook, D.J., Yu, Z.: Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(6), 790–808 (2012)CrossRefGoogle Scholar
  5. 5.
    Gravina, R., Alessandro, A., Salmeri, A., et al.: Enabling multiple BSN applications using the SPINE framework. In: IEEE 2010 International Conference on Body Sensor Networks (BSN), pp. 228–233. IEEE (2010)Google Scholar
  6. 6.
    Fortino, G., Guerrieri, A., Bellifemine, F.L., Giannantonio, R.: SPINE2: developing BSN applications on heterogeneous sensor nodes. In: IEEE Fourth International Symposium on Industrial Embedded Systems, pp. 128–131. IEEE (2009)Google Scholar
  7. 7.
    Wang, Z., Qiu, S., Cao, Z., Jiang, M.: Quantitative assessment of dual gait analysis based on inertial sensors with body sensor network. Sens. Rev. 33(1), 48–56 (2013)CrossRefGoogle Scholar
  8. 8.
    Mulling, K., Kober, J., Kroemer, O., Peters, J.: Learning to select and generalize striking movements in robot table tennis. Int. J. Robot. Res. 32(3), 263–279 (2013)CrossRefGoogle Scholar
  9. 9.
    Blank, P., HobBach, J., Schuldhaus, D., Eskofier, B.M.: Sensor-based stroke detection and stroke type classification in table tennis. In: Proceedings of the 2015 ACM International Symposium on Wearable Computers, pp. 93–100. ACM (2015)Google Scholar
  10. 10.
    Bufton, A., Campbell, A., Howie, E., Straker, L.: A comparison of the upper limb movement kinematics utilized by children playing virtual and real table tennis. Hum. Mov. Sci. 38, 84–93 (2014)CrossRefGoogle Scholar
  11. 11.
    Wang, Z., Guo, M., Zhao, C.: Badminton stroke recognition based on body sensor networks. IEEE Trans. Hum.-Mach. Syst. 46(5), 1–7 (2016)CrossRefGoogle Scholar
  12. 12.
    Pei, W., Wang, J., Xu, X., Wu, Z., Du, X.: An embedded 6-axis sensor based recognition for tennis stroke. In: IEEE International Conference on Consumer Electronics. IEEE (2017)Google Scholar
  13. 13.
    Zhang, Z.: Biomechanical analysis and model development applied to table tennis forehand strokes. Loughborough University (2017)Google Scholar
  14. 14.
    Maeda, T., Fujii, M., Hayashi, I., Tasaka, T.: Sport skill classification using time series motion picture data. In: Conference of the IEEE Industrial Electronics Society, pp. 5272–5277. IEEE (2015)Google Scholar
  15. 15.
    Blank, P., Kautz, T., Eskofier, B.M.: Ball impact localization on table tennis rackets using piezo-electric sensors. In: ACM International Symposium on Wearable Computers, pp. 72–79. ACM (2016)Google Scholar
  16. 16.
    Wang, Y., Chen, M., Wang, X., Chan, R.H., Li, W.J.: IoT for next-generation racket sports training. IEEE Internet Things J. 1 (2018)Google Scholar
  17. 17.
    Dimitriou, N., Delopoulos, A.: Motion-based segmentation of objects using overlapping temporal windows. Image Vis. Comput. 31(9), 593–602 (2013) CrossRefGoogle Scholar
  18. 18.
    Shi, G., Zou, Y., Li, W.J., Jin, Y., Pei, G.: Towards multi-classification of human motions using micro IMU and SVM training process. In: Advanced Materials Research, vol. 60–61, pp. 189–193 (2009)Google Scholar

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

Personalised recommendations