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.
Supported by National Natural Science Foundation of China under Grant No. 61873044, No. 61803072, No. 61903062 and No. 61473058, Dalian Science and Technology Innovation fund (2018J12SN077), National Defense Pre-Research Foundation under Grant 614250607011708, China Postdoctoral Science Foundation No. 2017M621131 and No. 2017M621132 and Fundamental Research Funds for the Central Universities under Grant DUT18RC(4)036.
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References
Mao, B.-J.: Different techniques comparison in biomechanical analysis of ping pong. Appl. Mech. Mater. 166–169, 3106–3109 (2012)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Wang, Z., Guo, M., Zhao, C.: Badminton stroke recognition based on body sensor networks. IEEE Trans. Hum.-Mach. Syst. 46(5), 1–7 (2016)
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)
Zhang, Z.: Biomechanical analysis and model development applied to table tennis forehand strokes. Loughborough University (2017)
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)
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)
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)
Dimitriou, N., Delopoulos, A.: Motion-based segmentation of objects using overlapping temporal windows. Image Vis. Comput. 31(9), 593–602 (2013)
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)
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Liu, R. et al. (2019). Table Tennis Stroke Recognition Based on Body Sensor Network. In: Montella, R., Ciaramella, A., Fortino, G., Guerrieri, A., Liotta, A. (eds) Internet and Distributed Computing Systems . IDCS 2019. Lecture Notes in Computer Science(), vol 11874. Springer, Cham. https://doi.org/10.1007/978-3-030-34914-1_1
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DOI: https://doi.org/10.1007/978-3-030-34914-1_1
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