Abstract
In this paper human recognition method is based on dynamic parameters of the human gait. In the method the artificial neural network algorithm is employed. Some parameters of the gait are defined in order to describe and recognize characteristics of gait for each considered individual. In classical approach to human recognition, description of the gait pattern is based on sequence of images analysis and biometric parameters. In this paper we present method based on determining some dynamic characteristics of the gait, which together with other kinematic determinants should allow to describe the unique gait pattern for each individual. Necessary data were obtained from system of motion analysis BTS and force plates, commonly used in biomechanics. All considered gait parameters were calculated from data which were obtained for 15 persons with different characteristic of the gait. To implement the recognition process the back-propagation neural network algorithm was used. In the paper three configurations of the input data (only kinematic parameters from the BTS system, only dynamic parameters from the force plates and both types of parameters together) are investigated and compared.
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
Alaqtash, M., Sarkodie-Gyan, T., Yu, H., Fuentes, O., Brower, R., Abdelgawad, A.: Automatic classification of pathological gait pattern using ground reaction forces and machine learning algorithms. In: Proceedings of the 33rd Annual International Conference of the IEEE EMBS, pp. 453–457 (2011)
Bartlett, R.: Artificial intelligence in sports biomechanics: new dawn or false hope? J. Sports Sci. Med. 5(4), 474–479 (2006)
Barton, J.G., Lees, A.: An application of neural networks for distinguishing gait pattern on the basis of hip-knee joint angle diagrams. Gait Posture 5(1), 28–33 (1997)
Chau, T.: A review of analytical techniques for gait data. Part 2: neural network and wavelet methods. Gait Posture 13(2), 102–120 (2001)
Hahn, M.E., Farley, A.M., Lin, V., Chou, L.-S.: Neural network estimation of balance control during locomotion. J. Biomech. 38(4), 717–724 (2005)
Inman, V.T., Ralston, H.J., Todd, F.: Human Walking. Williams & Wilkins (1981)
Lee, H., Lee, H., Kim, E.: A new gait recognition system based on hierarchical fair competition-based parallel genetic algorithm and selective neural network ensemble. Int. J. Control Autom. Syst. 12(1), 202–207 (2014)
McBridge, J., Zhang, S., Wortley, M., Paquette, M., Klipple, G., Byrd, E., Baumgartner, L., Zhao, X.: Neural network analysis of gait biomechanical data for classification of knee osteoarthritis, Biomech. Sci. Eng. Conf. (BSEC) 1–4 (2011)
Michalski, R., Wit, A., Gajewski, J.: Use of artificial neural networks for assessing parameters of gait symmetry. Acta Bioeng. Biomech. 13(4), 65–70
Narasimhulu, V.G., Jilani, S.A.K.: Back propagation neural network based gait recognition. Int. J. Comput. Sci. Inf. Technol. 3(5), 5025–5030 (2012)
Nixon, M.S., Tan, T., Chellappa, R.: Human Identification based on Gait. Springer (2006)
Oh, S.E., Choi, A., Mun, J.H.: Prediction of ground reaction forces during gait based on kinematics and a neural network model. J. Biomech. 16(14), 2372–2380 (2013)
Perl, J.: Artificial neural networks in motor control research. Clin. Biomech. 19(9), 873–875 (2004)
Schöllhorn, W.I.: Application of artificial neural nets in clinical biomechanics. Clin. Biomech. 19(9), 876–898 (2004)
Schöllhorn, W.I., Nigg, B.M., Stefanyshyn, D.J., Liu, W.: Identification of individual walking patterns using time descrete and time continuous data sets. Gait Posture 15(2), 180–186 (2002)
Shukla, R., Shukla, R., Shukla, A., Sharma, S., Tiwari, N.: Gender identification in human gait using neural network. Int. J. Mod. Educ. Comput. Sci. 11, 70–75 (2012)
Su, F-Ch., Wu, W.-L.: Design and testing of a genetic algorithm neural network in the assessment of gait patterns. Med. Eng. Phys. 22(1), 67–74 (2000)
Tafazzoli, F., Safabakhsh, R.: Model-based human gait recognition using leg and arm movements. Eng. Appl. Artif. Intell. 23(8), 1237–1246 (2010)
Tahir, N.M., Manap, H.H.: Parkinson disease gait classification based on machine learning approach. J. Appl. Sci. 12(2), 180–185 (2012)
Xiao, Q.: A note on computational intelligence methods in biometrics. Int. J. Biometrics 4(2), 180–188 (2012)
Yoo, J.-H., Hwang, D., Moon, K.-Y., Nixon, M.S.: Automated human recognition by gait using neural network. First Workshop on Image Processing Theory, Tools and Application, pp. 1–6 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Walczak, T., Grabski, J.K., Cieślak, M., Michałowska, M. (2016). The Recognition of Human by the Dynamic Determinants of the Gait with Use of ANN. In: Awrejcewicz, J. (eds) Dynamical Systems: Modelling. DSTA 2015. Springer Proceedings in Mathematics & Statistics, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-319-42402-6_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-42402-6_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42401-9
Online ISBN: 978-3-319-42402-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)