Abstract
A novel recognition method of throwing force of athlete combined with wavelet and multi-class support vector machine is introduced in the paper, which is based on the analysis of motion characters of gliding shot put. Utilizing the digital shot based on a three dimensional accelerometer, we get the three dimensional throwing forces in real time. Through wavelet transform, the general characteristics of force information are picked up. Then the general characteristics are input into the classifier for recognition of throwing force curves. The analysis provides the scientific basis for the motion training and instruction of shot put. The experiment shows that the method not only has high anti-noise ability and improves the recognition efficiency, but also decreases the burden of system and improves the recognition speed.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Sun, W.Q., Song, G.M., Qiu, L.K., Shen, C.S., Tang, Y., Ge, Y.J.: Design and Implementation of Digital-Shot Based on Three-Axis Accelerometer. Computer Engineering and Applications 41(4), 102–104 (2005)
Berbyuk, V., Lytvyn, B.A.: Mathematical Modeling of Human Walking on the Basis of Optimization of Controlled Processes in Biodynamical Systems. Journal of Mathematical Sciences 104(5), 1575–1586 (2001)
Hahn, M.E., Farley, A.M., Lin, V., Chou, L.-S.: Neural Network Estimation of Balance Control during Locomotion. Journal of Biomechanics 38(4), 717–724 (2005)
Tang, Y., Ge, Y.J., Yuan, H.Y., Wang, D.C.: Application of FNN in the Recognition of Force of Foot about Athlete. Journal of System Simulation 15(10), 1412–1417 (2003)
Yuan, H.Y., Tang, Y., Song, G.M., Song, Q.J., Ge, Y.J., Liu, J.G.: Development and Application of Digital Shot Sensor. Journal of Transducer Technology 23(9), 61–63 (2004)
Lu, D.M.: The Measurement of Sport Biomechanics, 1st edn. Beijing Sport University Press (2001)
An, J.L., Wang, Z.O., Ma, Z.P.: A New SVM Multiclass Classification Method. Information and Control 33(3), 262–267 (2004)
Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. Journal of Machine Learning 1(1), 113–141 (2000)
Jeronimo, A.-G., Fernando, P.-C.: Multi-class Support Vector Machines: A New Approach. In: Digital Object Identifier, IEEE ICASSP, vol. 2, pp. 781–784 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, J., Ge, Y., Lei, J., Song, Q., Ge, Y., Yu, Y. (2006). Recognition Method of Throwing Force of Athlete Based on Multi-class SVM. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_37
Download citation
DOI: https://doi.org/10.1007/11760023_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
eBook Packages: Computer ScienceComputer Science (R0)