Gesture recognition with a 3-D accelerometer

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5585 LNCS, Issue , 2009, Pages 25-38

  Wu, Jiahui ^a   Pan, Gang ^a   Zhang, Daqing ^b   Qi, Guande ^a   Li, Shijian ^a  

^a ZHEJIANG UNIVERSITY   (China)

^b TELECOM SUDPARIS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

3-DIMENSIONAL; A-FRAMES; ACCELERATION DATA; DATA SETS; DESCRIPTORS; GESTURE FEATURES; GESTURE-BASED INTERACTION; MULTI-CLASS; MULTICLASS SVM; RECOGNITION RATES; UBIQUITOUS COMPUTING ENVIRONMENT; USER-DEPENDENT;

ACCELEROMETERS; COMMUNICATION CHANNELS (INFORMATION THEORY); HUMAN COMPUTER INTERACTION; KNOWLEDGE MANAGEMENT; SUPPORT VECTOR MACHINES; UBIQUITOUS COMPUTING;

GESTURE RECOGNITION;

EID: 71049142212     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02830-4_4     Document Type: Conference Paper

References (24)

1. 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)
2. Tsukada, K., Yasumura, M.: Ubi-Finger: Gesture Input Device for Mobile Use. In: Proceedings of APCHI 2002, vol. 1, pp. 388-400 (2002)
3. 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)
4. 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)
5. 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)
6. Mäntylä, V.-M.: Discrete Hidden Markov Models with Application to Isolated User-Dependent Hand Gesture Recognition. VTT publications (2001)
7. Hofmann, F.G., Heyer, P., Hommel, G.: Velocity profile based recognition of dynamic gestures with discrete hidden markov models. In: Wachsmuth, I., Frohlich, M. (eds.) GW 1997. LNCS, vol. 1371, pp. 81-95. Springer, Heidelberg (1998)
8. Ravi, N., Dandekar, N., Musore, P., Littman, M.: Activity Recognition from Accelerome-ter Data. In: Proceedings of IAAI 2008, July 2005, pp. 11-18 (2005)
9. Frigo, M., Johnson, S.G.: The Design and implementation of FFTW3. Proceedings of the IEEE 93(2) (2005)
10. Joachims, T.: Making large-Scale SVM Learning Practical. In: Schollkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. MIT-Press, Cambridge (1999)
11. Christanini, J., Taylor, J.S.: An Introduction to Support Vector Machines and other Kernel-based Methods. Cambridge University Press, Cambridge (2000)
12. Mitra, S., Acharya, T.: Gesture Recognition: A Survey. IEEE Trans. Systems, Man, and Cybernetics, Part C 37(3), 311-324 (2007)
13. Quinlan, J.R.: Improved use of continuous attributes in c4.5. Journal of Artificial Intelligence Research 4, 77-90 (1996)
14. Moghaddam, B., Yang, M.-H.: Learning Gender with Support Faces, IEEE Trans. Pattern Analysis and Machine Intelligence 24(5), 707-711 (2002)
15. 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)
16. 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)
17. 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)
18. Liu, J., Wang, Z., Zhong, L., Wickramasuriya, J., Vasudevan, V.: uWave: Accelerometer-based Personalized Gesture Recognition and Its Applications. In: IEEE PerCom 2009 (2009)
19. Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Fer-scha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1-17. Springer, Heidelberg (2004)
20. Wilson, D.H., Wilson, A.: Gesture Recognition using the XWand, Technical Report CMU-RI-TR-04-57, CMU Robotics Institute (2004)
21. Apple iPhone, http://www.apple.com/iphone
22. Nintendo Wii, http://www.nintendo.com/wii
23. 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)
24. Kela, J., Korpipaa, P., Mantyjarvi, J., Kallio, S., Savino, G., Jozzo, L., Marca, D.: Accele-rometer-based gesture control for a design environment. Personal Ubiquitous Computing 10, 285-299 (2006)