Abstract
In this work the Spiking Neural Networks (SNNs) for the classification of ways of walking patterns is presented. The Differential Evolution (DE) Algorithm as an optimization technique was used for weights and delays settings. Two accelerometers, each one with three axes, were used to obtain simultaneous information on both legs. The information formed by nine features has been stored in a database: the first three correspond to the accelerations of x, y and z axis, next three correspond to the velocities which are obtained by doing an integration of the acceleration data for each axis and finally the positions x, y and z are calculated by the integration of velocities respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
S. Chernbumroong, S. Cang, A. Atkins, H. Yu, Elderly activities recognition and classification for applications in assisted living. Expert Syst. Appl. 40(5), 1662–1674 (2013)
L. Bao, S.S. Intille, in Activity Recognition from User-Annotated Acceleration Data, ed. by A. Ferscha, F. Mattern. Pervasive Computing. Pervasive 2004. Lecture Notes in Computer Science, vol 3001 (Springer, Berlin, Heidelberg, 2004)
U. Maurer, A. Smailagic, D.P. Siewiorek, M. Deisher, Activity recognition and monitoring using multiple sensors on different body positions, wearable and implantable body sensor networks, in BSN 2006, International Workshop on (2006), pp. 4–7
D. Sprute, K. Matthias, On-chip activity recognition in a smart home, in 12th International Conference on Intelligent Environments (IE) (2016), pp. 95–102
M. Friedman, A. Kandel, Introduction to Pattern Recognition—Statistical, Structural, Neural and Fuzzy Logic Approaches (London, 1999), p. 345
G. Ou, Y.L. Murphey, Multi-class pattern classification using neural networks. Pattern Recognit. 40(1), 4–18 (2007)
T.H. Oong, N. Ashidi, M. Isa, Adaptive evolutionary artificial neural networks for pattern classification. IEEE Trans. Neural Netw. 22(11), 1823–1836 (2011)
S. Ghosh-dastidar, H. Adeli, Spiking neural networks. Int. J. Neural Sys. 19, 295–308 (2009). https://dx.doi.org/10.1142/S0129065709002002
A. Belatreche, L.P. Maguire, Advances in design and application of spiking neural networks. Comput. A Fusion Found. Methodologies Appl. 11, 239–248 (2007)
R.A. Vazquez, A. Cachón, Integrate and fire neurons and their application in pattern recognition. CCE, pp. 424–428 (2010)
V. Feoktistov, Differential Evolution (New York, 2006)
Acknowledgements
This work was partially supported by CONACYT and León Institute of Technology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Mares, K.F., Baltazar, R., Casillas, M.Á., Zamudio, V., Lemus, L. (2018). A Proposal to Classify Ways of Walking Patterns Using Spiking Neural Networks. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-71008-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-71008-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71007-5
Online ISBN: 978-3-319-71008-2
eBook Packages: EngineeringEngineering (R0)