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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29396

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Dynamic Time Warping in Gait Classificationof Motion Capture Data
The method of gait identification based on the nearest neighbor classification technique with motion similarity assessment by the dynamic time warping is proposed. The model based kinematic motion data, represented by the joints rotations coded by Euler angles and unit quaternions is used. The different pose distance functions in Euler angles and quaternion spaces are considered. To evaluate individual features of the subsequent joints movements during gait cycle, joint selection is carried out. To examine proposed approach database containing 353 gaits of 25 humans collected in motion capture laboratory is used. The obtained results are promising. The classifications, which takes into consideration all joints has accuracy over 91%. Only analysis of movements of hip joints allows to correctly identify gaits with almost 80% precision.
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[1] Boyd J.E. Little J.J. Biometric Gait Identification, Lecture Notes in Computer Science 3161 Springer 2005.
[2] Świtoński, A., Mucha, R., Danowski, D., Mucha, M., Cieślar, G., Wojciechowski, K., Sieroń, A., Human identification based on a kinematical data of a gait, Electrical Review, 2011.
[3] Poppe R., Vision-based human motion analysis: An overview, Computer Vision and Image Understanding, 2007.
[4] Pushpa M., Arumugamz G., An Efficient Gait Recognition System For Human Identification Using Modified ICA, International Journal of Computer Science and Information Technology, vol. 2, no. 1, 2010
[5] Liang W., Tieniu T., , Huazhong N., and Weiming H., Silhouette Analysis-Based Gait Recognition for Human Identification, IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 25, no. 12, 2003.
[6] M. Cheng, M. Ho, C.Huang , Gait Analysis For Human Identification Through Manifold Learning and HMM, Pattern Recognition, Volume 41 Issue 8, 2541-2553, 2008.
[7] Świtoński, A., Polański, A., Wojciechowski, K. Human identification based on the reduced kinematic data of the gait, IEEE 7th International Symposium on Image and Signal Processing and Analysis, 2011.
[8] Świtoński, A., Polański, A., Wojciechowski, K. Human identification based on gait paths , Advanced Concepts for Intelligent Vision Systems, LNCS, 2011.
[9] Zonghua Zhang,Nikolaus F Troje:, View-independent person identification from human gait, Neurocomputing 69, 2005
[10] Pogorelc B., Gams M., Diagnosing Health Problems from Gait Patterns of Elderly, Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, pp. 2238 - 2241, ISBN: 978-1-4244-4123-5.
[11] Zifchock R., Davis I., Higginson J., Royer T., The symmetry angle: A novel, robust method of quantifying asymmetry, Gait & Posture, Volume 27, Issue 4, May 2008, Pages 622-627.
[12] Omar S. Mian, Susanne A. Schneider, Petra Schwingenschuh, Kailash P. Bhatia and Brian L. Day, Gait in SWEDDs Patients: Comparison with Parkinson-s Disease Patients and Healthy Controls, Movement Disorders, (2011), DOI: 10.1002/mds.23684.
[13] Lakany H., Extracting a diagnostic gait signature, Pattern Recognition, Volume 41, Issue 5, May 2008, Pages 1627-1637disease, Clinical Biomechanics, Volume 16, Issue 6, July 2001, Pages 459-470.
[14] Muller M., Roder T.: A Relational Approach to Content-based Analysis of Motion Capture Data. Vol. 36 of Computational Imaging and Vision, ch. 20, 477-506, 2007.
[15] Kale A., Sundaresan A., Rajagopalan A. N., Cuntoor N. P., Roy- Chowdhury A. K., Kr├╝ger V., Chellappa R.: Identification of Humans Using Gait, IEEE Transactions On Image Processing, Vol. 13, No. 9, 2004.
[16] Cheng M., Ho M., Huang C. , Gait Analysis For Human Identification Through Manifold Learning and HMM.
[17] Krzeszkowski T., Michalczuk A., Switonski A., Josiński H, Kwolek B., Markerless 3D Human Motion Capture for Gait Characterization and Recognition, ICCVG, LNCS, 2012.
[18] Myers C., Rabiner L., Rosenberg A., \Performance tradeo_s in dynamic time warping algorithms for isolated word recognition,"Acoustics, Speech, and Signal Processing
[see also IEEE Transactions onSignal Processing], IEEE Transactions on, vol. 28, no. 6, pp. 623{635,1980.
[19] Sakoe H., Chiba S., \Dynamic programming algorithm optimization for spoken word recognition," Acoustics, Speech and Signal Processing, IEEE Transactions on, vol. 26, no. 1, pp. 43{49, 1978.
[20] Kovar L., Gleicher M., Pighin F.. Motion graphs. ACM, Trans. Graph., 2002.
[21] Johnson M. Exploiting Quaternions to Support Expressive Interactive CharacterMotion. PhD thesis, Massachusetts Institute of Technology, 2003.
[22] Keogh J., Pazzan M J., Derivative Dynamic Time Warping, First SIAM International Conference on Data Mining, 2001.
[23] Kulbacki M., Segen J., Bak A., Unsupervised Learning Motion Models Using Dynamic Time Warping, Proceedings of the IIS'2002 Symposium on Intelligent Information Systems, 2002.
[24] Munich M., Perona P., Continous Dynamic Time Warrping for trnaslation-invariant curve alignmentwith applicatiomn to signature verification, Proc. of the 7th International Conference on Computer Vision (ICCV-99), Korfu, Greece, September, 1999.
[25] Zhou F., de la Torre F., Canonical Time Warping for Alignment of Human Behavior, Neural Information Processing Systems, 2009.
[26] Hold G.A., Reinder M.J., Hendrics E.A., Multi-Dimensional Dynamic Time Warping for Gesture Recognition, Thirteenth annual conference of the Advanced School for Computing and Imaging, 2007.
[27] Martin M., Maycock J., Schmidt P., Kramer O., Recognition of Manual Actions Using Vector Quantization and Dynamic Time Warping Lecture Notes in Computer Science, 2010, Volume 6076/2010.
[28] Kale A.A., Cuntoor N.P., Yegnanarayana B., Rajagopalan A.N., Chellappa R., "Gait Analysis for Human Identification", ;in Proc. AVBPA, 2003, pp.706-714.
[29] Boulgouris N. V., Plataniotis K. N., Hatzinakos, D. Gait Recognition Using Dynamic Time Warping, IEEE International Workshop on Multimedia Signal Processing ,Sienna, Italy, September 2004.
[30] Sakoe H., Chiba S., Dynamic Programming Algorihtm Optimization for Spoken Word Recognition, IEEE Transactions on Acoustics, Speech and Signal Processing Vol.. ASSP-26, No. 1, 1978.
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