Abstract
Gesture-based interaction, as a natural way for human-computer interaction, has a wide range of applications in ubiquitous computing environment. This paper presents an acceleration-based gesture recognition approach, called FDSVM (Frame-based Descriptor and multi-class SVM), which needs only a wearable 3-dimensional accelerometer. With FDSVM, firstly, the acceleration data of a gesture is collected and represented by a frame-based descriptor, to extract the discriminative information. Then a SVM-based multi-class gesture classifier is built for recognition in the nonlinear gesture feature space. Extensive experimental results on a data set with 3360 gesture samples of 12 gestures over weeks demonstrate that the proposed FDSVM approach significantly outperforms other four methods: DTW, Naïve Bayes, C4.5 and HMM. In the user-dependent case, FDSVM achieves the recognition rate of 99.38% for the 4 direction gestures and 95.21% for all the 12 gestures. In the user-independent case, it obtains the recognition rate of 98.93% for 4 gestures and 89.29% for 12 gestures. Compared to other accelerometer-based gesture recognition approaches reported in literature FDSVM gives the best resulrs for both user-dependent and user-independent cases.
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
Schlömer, T., Poppinga, B., Henze, N., Boll, S.: Gesture Recognition with a Wii Controller. In: International Conference on Tangible and Embedded Interaction (TEI 2008), Bonn Germany, Feburary 18-20, pp. 11–14 (2008)
Tsukada, K., Yasumura, M.: Ubi-Finger: Gesture Input Device for Mobile Use. In: Proceedings of APCHI 2002, vol. 1, pp. 388–400 (2002)
Sawada, H., Hashimoto, S.: Gesture Recognition Using an Accelerometer Sensor and Its Application to Musical Performance Control. Electronics and Communications in Japan Part 3, 9–17 (2000)
Mäntylä, V.-M., Mäntyjärvi, J., Seppänen, T., Tuulari, E.: Hand Gesture Recognition of a Mobile Device User. In: Proceedings of the International IEEE Conference on Multimedia and Expo., pp. 281–284 (2000)
Mäntyjärvi, J., Kela, J., Korpipää, P., Kallio, S.: Enabling fast and effortless customization in accelerometer based gesture interaction. In: Proceedings of the 3rd International Conference on Mobile and Ubiquitous Multimedia (MUM 2004), October 27-29, pp. 25–31. ACM Press, New York (2004)
Mäntylä, V.-M.: Discrete Hidden Markov Models with Application to Isolated User-Dependent Hand Gesture Recognition. VTT publications (2001)
Hofmann, F.G., Heyer, P., Hommel, G.: Velocity profile based recognition of dynamic gestures with discrete hidden markov models. In: Wachsmuth, I., Fröhlich, M. (eds.) GW 1997. LNCS, vol. 1371, pp. 81–95. Springer, Heidelberg (1998)
Ravi, N., Dandekar, N., Musore, P., Littman, M.: Activity Recognition from Accelerometer Data. In: Proceedings of IAAI 2008, July 2005, pp. 11–18 (2005)
Frigo, M., Johnson, S.G.: The Design and implementation of FFTW3. Proceedings of the IEEE 93(2) (2005)
Joachims, T.: Making large-Scale SVM Learning Practical. In: Schöllkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods – Support Vector Learning. MIT-Press, Cambridge (1999)
Christanini, J., Taylor, J.S.: An Introduction to Support Vector Machines and other Kernel-based Methods. Cambridge University Press, Cambridge (2000)
Mitra, S., Acharya, T.: Gesture Recognition: A Survey. IEEE Trans. Systems, Man, and Cybernetics, Part C 37(3), 311–324 (2007)
Quinlan, J.R.: Improved use of continuous attributes in c4.5. Journal of Artificial Intelligence Research 4, 77–90 (1996)
Moghaddam, B., Yang, M.-H.: Learning Gender with Support Faces, IEEE Trans. Pattern Analysis and Machine Intelligence 24(5), 707–711 (2002)
Osuna, E., Freund, R., Girosi, F.: Training Support Vector Machines: An Application to Face Detection. In: Proc. IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition, pp. 130–136 (1997)
Cho, S.-J., Choi, E., Bang, W.-C., Yang, J., Sohn, J., Kim, D.Y., Lee, Y.-B., Kim, S.: Two-stage Recognition of Raw Acceleration Signals for 3D-Gesture-Understanding Cell Phones. In: 10th International Workshop on Frontiers in Handwriting Recognition (2006)
Niezen, G., Hancke, G.P.: Gesture recognition as ubiquitous input for mobile phones. In: International Workshop on Devices that Alter Perception (DAP 2008), conjunction with Ubicomp 2008 (2008)
Liu, J., Wang, Z., Zhong, L., Wickramasuriya, J., Vasudevan, V.: uWave: Accelerometer-based Personalized Gesture Recognition and Its Applications. In: IEEE PerCom 2009 (2009)
Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)
Wilson, D.H., Wilson, A.: Gesture Recognition using the XWand, Technical Report CMU-RI-TR-04-57, CMU Robotics Institute (2004)
Apple iPhone, http://www.apple.com/iphone
Nintendo Wii, http://www.nintendo.com/wii
Hommel, G., Hofmann, F.G., Henz, J.: The TU Berlin High-Precision Sensor Glove. In: Proceedings of the WWDU 1994, Fourth International Scientific Conference, vol. 2, pp. 47–49. University of Milan, Milan (1994)
Kela, J., Korpipaa, P., Mantyjarvi, J., Kallio, S., Savino, G., Jozzo, L., Marca, D.: Accelerometer-based gesture control for a design environment. Personal Ubiquitous Computing 10, 285–299 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, J., Pan, G., Zhang, D., Qi, G., Li, S. (2009). Gesture Recognition with a 3-D Accelerometer. In: Zhang, D., Portmann, M., Tan, AH., Indulska, J. (eds) Ubiquitous Intelligence and Computing. UIC 2009. Lecture Notes in Computer Science, vol 5585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02830-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-02830-4_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02829-8
Online ISBN: 978-3-642-02830-4
eBook Packages: Computer ScienceComputer Science (R0)