Abstract
The purpose of our study is to construct a system where a user can register his/her own arm gestures as templates and entered gestures are recognized precisely on a real-time basis. In order to realize them, we propose the following method: (1) 3D positions of both user’s wrists are captured by using a Kinect sensor. (2) Sequences of motion vectors of both wrists are detected from the time series 3D position data. (3) The entered gesture is recognized based on the similarity between the entered gesture and each template. The similarity can be calculated by applying continuous DP matching to the sequences of motion vectors of them. The templates are gestures which are recorded by the user before the recognition process. In our experiments, good results were obtained.
Chapter PDF
Similar content being viewed by others
References
Wu, J., Pan, G., Zhang, D., Qi, G., Li, S.: Gesture Recognition with a 3-D Accelerometer. In: Zhang, D., Portmann, M., Tan, A.-H., Indulska, J. (eds.) UIC 2009. LNCS, vol. 5585, pp. 25–38. Springer, Heidelberg (2009)
Kohn, B., Belbachir, A.N., Hahn, T., Kaufmann, H.: Event-driven body motion analysis for real-time gesture recognition. In: IEEE International Symposium on Circuits and Systems (ISCAS) 2012, pp. 703–706 (2012)
Schlomer, T., Poppinga, B., Henze, N., Boll, S.: Gesture Recognition with a Wii Controller. In: Proceedings of the 2nd International Conference on Tangible and Embedded Interaction, TEI 2008, pp. 11–14 (2008)
Kwon, D.Y., Gross, M.: A Framework for 3D Spatial Gesture Design and Modeling Using a Wearable Input Device. In: Proceedings of the 11th IEEE International Symposium on Wearable Computers, pp. 23–26 (2007)
Develop for Kinect | Microsoft Kinect for Windows, http://www.microsoft.com/en-us/kinectforwindows/
Murakami, K., Taguchi, H.: Gesture recognition using recurrent neural networks. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 1991, pp. 237–242 (1991)
Chen, F.S., Fu, C.M., Huang, C.L.: Hand gesture recognition using a real-time tracking method and hidden Markov models. Image and Vision Computing 21(8-1), 745–758 (2003)
Oka, R.: Spotting Method for Classification of Real World Data. The Computer Journal 41(8), 559–565 (1998)
Serra, B., Berthod, M.: Subpixel Contour Matching Using Continuous Dynamic Programming. In: Proceedings of CVPR 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 202–207 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yamazaki, K., Miyao, H., Maruyama, M. (2013). Arm Gesture Recognition Using Continuous DP for User-Defined Gestures. In: Stephanidis, C. (eds) HCI International 2013 - Posters’ Extended Abstracts. HCI 2013. Communications in Computer and Information Science, vol 373. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39473-7_78
Download citation
DOI: https://doi.org/10.1007/978-3-642-39473-7_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39472-0
Online ISBN: 978-3-642-39473-7
eBook Packages: Computer ScienceComputer Science (R0)