Abstract
This paper proposes a distinguishing method based on Doppler feature analysis and extraction in human gait classification. The author first analyzes the characteristics of human gaits. Then, based on analysis of human Doppler features during walking and running, the paper extracts micro-Doppler parameters that can be used in motion identification. Finally, results are presented according to the classification of human gaits based on a support vector machine (SVM) classifier.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lee, L., Crimson, W.E.L.: Gait analysis for recognition and classification. In: The 5th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 148–155. IEEE, Piscataway (2002)
Ren, L.Q., Liang, Y.H.: Preliminary studies on the basic factors of bionics. Sci. China Technol. Sci. 3, 148–155 (2014)
Chen, V.C., Li, F.Y., Ho, S.S.: Micro-doppler effect in radar-phenomenon, model and simulation study. IEEE Trans. Aerosp. Electron. Syst. 42(1), 2–21 (2006)
Lawrence, S., Marple, J.: Sharpening and bandwidth extrapolation techniques for radar micro-Doppler feature extraction. In: IEEE International Conference on Radar, pp. 166–170. IEEE, Piscataway, Adelaide, Australia (2003)
Greneker, E.F., Sylveste, V.B.: Use of the envelope detection method to detect micro-Doppler Orlando. In: The SPIE on Passive Millimeter-Wave Imaging Technology VI and Radar Sensor Technology VII.SPIE Proceedings. 5077, 167–174 (2003)
Zhang, X.J., Xu, C., Li, M., et al.: Sparse and low-rank coupling image segmentation model via nonconvex regularization. Int. J. Pattern Recognit Artif Intell. 23, 234–237 (2015)
Albuquerque, P., Alfinito, S., Torres, C.V.: Support vector clustering for customer segmentation on mobile tv service. Commu. in Stat. – Simul. and Comput. 5, 212–216 (2015)
Wu, T., Bae, M.H., Zhang, M., et al.: A prior feature SVM-MRF based method for mouse brain segmentation. Neuro Image. 23, 234–237 (2011)
Kramer, K.A., Lawrence, O.H., Goldgof, D.B et al.: Fast support vector machines for continuous data. IEEE Trans. Syst. Man Cybern. Part B Cybern. Publ. IEEE Syst. Man Cybern. Soc. 4, 478–492, (2009)
Luo, W.L., Moreira, L., Guedes Soares, C.: Manoeuvring simulation of catamaran by using implicit models based on support vector machines. Ocean Eng. 12, 150–159 (2014)
Doumpos, M., Zopounidis, C., Golfinopoulou, V.: Additive support vector machines for pattern classification. IEEE Trans. Syst. Man Cybern. B Cybern. 775, 1344–1350 (2015)
Lin, X.Q., Gao, Z., Lin, Y.M., Zhan, X.S.: Human Action Recognition Using Transformation and SVM. In : World Conference on Control, Electronics and Electrical Engineering(WCEE 2015) WIT Press, Shanghai, China (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Wang, X., He, JW., Chen, Z., Zhu, G. (2016). Human Gait Classification Using Doppler Motion Feature Analysis. In: Hussain, A. (eds) Electronics, Communications and Networks V. Lecture Notes in Electrical Engineering, vol 382. Springer, Singapore. https://doi.org/10.1007/978-981-10-0740-8_26
Download citation
DOI: https://doi.org/10.1007/978-981-10-0740-8_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0738-5
Online ISBN: 978-981-10-0740-8
eBook Packages: EngineeringEngineering (R0)