Abstract
Distinguishing tip-toe walking from normal walking, in human locomotion patterns, becomes important in applications such as Autism disorder identification. In this paper, we propose a novel approach for tip-toe walking detection based on the walk’s Central Pattern Generator (CPG) parameters. In the proposed approach, the tip-toe walking is modeled by a CPG. Then, the motions of subjects are recorded and skeleton data are extracted using the first-generation Microsoft Kinect sensor. The CPG parameters of these motions are determined and compared to the given patterns to distinguish between tip-toe walking and normal walking. The accuracy of classification is promising while further data will improve the accuracy rate.
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
Similar content being viewed by others
References
Pantic, M., Pentland, A., Nijholt, A., Huang, T.S.: Human computing and machine understanding of human behavior: a survey. In: Huang, T.S., Nijholt, A., Pantic, M., Pentland, A. (eds.) Artifical Intelligence for Human Computing. LNCS, vol. 4451, pp. 47–71. Springer, Heidelberg (2007). doi:10.1007/978-3-540-72348-6_3
Brdiczka, O., Langet, M., Maisonnasse, J., Crowley, J.L.: Detecting human behavior models from multimodal observation in a smart home. IEEE Trans. Autom. Sci. Eng. 6, 588–597 (2009)
Pentland, A.: Looking at people: sensing for ubiquitous and wearable computing. IEEE Trans. Pattern Anal. Mach. Intell. 22, 107–119 (2000)
Sung, J., Ponce, C., Selman, B., Saxena, A.: Human activity detection from RGBD images. In: IEEE International Conference on Robotics and Automation, pp. 842–849 (2011)
Zhang, C., Tian, Y.: RGB-D camera-based daily living activity recognition. J. Comput. Vis. Image Process. 2, 12 (2012)
Yang, X., Tian, Y.: Effective 3D action recognition using Eigenjoints. J. Vis. Commun. Image Represent. 25, 2–11 (2014)
Steele, R., Lo, A., Secombe, C., Wong, Y.K.: Elderly persons’ perception and acceptance of using wireless sensor networks to assist healthcare. Int. J. Med. Inform. 78, 788–801 (2009)
Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54, 2787–2805 (2010)
León, O., Hernández-Serrano, J., Soriano, M.: Internet of things. Int. J. Commun. Syst. 23, 633–652 (2010)
Weber, R.H., Weber, R.: Internet of Things. Springer, Heidelberg (2010). doi:10.1007/978-3-642-11710-7
Zhang, Z.: Microsoft kinect sensor and its effect. IEEE Multimed. 19, 4–10 (2012)
Bell, C.C.: DSM-IV: diagnostic and statistical manual of mental disorders. JAMA 272, 828–829 (1994)
Zhang, H., Parker, L.E.: 4-Dimensional local spatio-temporal features for human activity recognition. In: IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 2044–2049 (2011)
Popa, M., Kemal Koc, A., Rothkrantz, L.J.M., Shan, C., Wiggers, P.: Kinect sensing of shopping related actions. In: Wichert, R., Van Laerhoven, K., Gelissen, J. (eds.) AmI 2011. CCIS, vol. 277, pp. 91–100. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31479-7_16
Ebrahimi, M., Feghi, M., Moradi, H., Mirian, M., Pouretemad, H.: Distinguishing tip-toe walking from normal walking using skeleton data gathered by 3D sensors, pp. 1–6 (2015)
Bucher, D., Haspel, G., Golowasch, J., Nadim, F., Bucher, D., Haspel, G., Golowasch, J., Nadim, F.: Central pattern generators. Encyclopedia of Life Sciences, pp. 1–12. Wiley, Chichester, UK (2015)
Grillner, S., Wallen, P.: Central pattern generators for locomotion, with special reference to vertebrates. Annu. Rev. Neurosci (2003). doi:10.1146/annurev.ne.08.030185.001313
Ijspeert, A.J.: Central pattern generators for locomotion control in animals and robots: a review. Neural Netw. 21, 642–653 (2008)
Righetti, L., Ijspeert, A.J.: Pattern generators with sensory feedback for the control of quadruped locomotion. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 819–824 (2008)
Righetti, L., Ijspeert, A.J.: Programmable central pattern generators: an application to biped locomotion control. In: Proceedings IEEE ICRA, pp. 1585–1590 (2006)
Acknowledgments
This research is partially funded by Cognitive Sciences and Technologies Council (COGC) of Iran. Special thanks to Dr. Babak Nadjar Araabi and Dr. Hamid Reza Pour Etemad and Centre for the Treatment of Autistic Disorders (CTAD) for their help. We want to thank all members of Advanced Robotics and Intelligent Systems (ARIS) Lab for their generous help.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Taban, R., Parsa, A., Moradi, H. (2017). Tip-Toe Walking Detection Using CPG Parameters from Skeleton Data Gathered by Kinect. In: Ochoa, S., Singh, P., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science(), vol 10586. Springer, Cham. https://doi.org/10.1007/978-3-319-67585-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-67585-5_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67584-8
Online ISBN: 978-3-319-67585-5
eBook Packages: Computer ScienceComputer Science (R0)