Abstract
In this paper we present a new approach, based on the kinetic status history, to automatically determine the starting and ending instants of human dynamic gestures. This method opens up the possibility to distinguish static or quasi-static poses from dynamic actions, during a real-time human motion capture. This way a more complex Human-Computer Interaction (HCI) can be attained. Along with this procedure, we also present a novel method to recognize dynamic gestures independently from the velocity with which they have been performed. The efficiency of this approach is tested with gestures captured with a triple axis accelerometer, and recognized with different statistical classifiers, obtaining satisfactory results for real-time applications.
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
Preview
Unable to display preview. Download preview PDF.
References
Min, B.W., Yoon, H.S., Soh, J., Ohashi, T., Ejima, T.: Visual recognition of static dynamic gesture: Gesture-driven editing system. Journal Of Visual Languages And Computing 10(3), 291–309 (1999)
Chang, C.C., Chen, J.J., Tai, W.K., Han, C.C.: New approach for static gesture recognition. Journal Of Information Science And Engineering 22(5), 1047–1057 (2006)
Essa, I.A., Pentland, A.P.: Coding, analysis, interpretation, and recognition of facial expressions. IEEE Transactions On Pattern Analysis And Machine Intelligence 19(7), 757–763 (1997)
Donato, G., Bartlett, M.S., Hager, J.C., Ekman, P.: Classifying facial actions. IEEE Transactions On Pattern Analysis And Machine Intelligence 21(10), 974–989 (1999)
Apple Inc. iPhone mobile phone at, http://www.apple.com/iphone/
Nintendo WiiTMconsole at, http://www.wii.com/
Sony Ericsson W910i mobile phone at, http://www.sonyericsson.com/cws/products/mobilephones/overview/w910i/
Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-time Tracking of the Human Body. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (1997)
Theobalt, C., Magnor, M., Schueler, P., Seidel, H.P.: Combining 2D Feature Tracking and Volume Reconstruction for Online Video-Based Human Motion Capture. In: Proceedings of Pacific Graphics, Beijing, China, pp. 96–103 (2002)
Varona, J., Buades, J.M., Perales, F.J.: Hands and face tracking for VR applications. International Journal of Systems & Applications in Computer Graphics 29, 179–187 (2005)
Lee, H.-K., Kim, J.H.: An HMM-Based Threshold Model Approach for Gesture Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(10), 961–973 (1999)
Rose, R.C.: Discriminant Wordspotting Techniques for Rejection Non-Vocabulary Utterances in Unconstrained Speech. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, San Francisco, USA, vol. 2, pp. 105–108 (1992)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Dasarathy, B.V.: KNN: Nearest Neighbor Pattern Classification Techniques. Edited collection. IEEE Press, California (1991)
Ibarguren, A., Maurtua, I., Sierra, B.: Recognition of sign language in real time through Data Gloves. In: Borrajo, D., Castillo, L., Corchado, J.M. (eds.) CAEPIA 2007. LNCS (LNAI), vol. 4788, pp. 307–316. Springer, Heidelberg (2007)
Analog Devices’ iMEMS®acceletometers at, http://www.analog.com/
Shawe-Taylor, J., Cristianini, N.: Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Fukunaga, K.: Introduction to statistical pattern recognition. Academic Press Professional Inc., San Diego (1990)
Stone, M.: Cross-validation choice and assessment of statistical procedures. Journal of Royal Statistical Society 36, 111–147 (1974)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Unzueta, L., Mena, O., Sierra, B., Suescun, Á. (2008). Kinetic Pseudo-energy History for Human Dynamic Gestures Recognition. In: Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO 2008. Lecture Notes in Computer Science, vol 5098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70517-8_38
Download citation
DOI: https://doi.org/10.1007/978-3-540-70517-8_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70516-1
Online ISBN: 978-3-540-70517-8
eBook Packages: Computer ScienceComputer Science (R0)