Construction of Symbolic Representation from Human Motion Information
In general, avatar-based communication has a merit that it can represent non-verbal information. The simplest way of representing the non-verbal information is to capture the human action/motion by a motion capture system and to visualize the received motion data through the avatar. However, transferring raw motion data often makes the avatar motion unnatural or unrealistic because the body structure of the avatar is usually a bit different from that of the human beings. We think this can be solved by transferring the meaning of motion, instead of the raw motion data, and by properly visualizing the meaning depending on characteristics of the avatar’s function and body structure. Here, the key issue is how to symbolize the motion meanings. Particularly, the problem is what kind of motions we should symbolize. In this paper, we introduce an algorithm to decide the symbols to be recognized referring to accumulated communication data, i.e., motion data.
KeywordsMotion Data Independent Component Analysis Symbolic Representation Motion Information Label Pattern
Unable to display preview. Download preview PDF.
- 1.Arita, D., Yoshimatsu, H., Hayama, D., Kunita, M., Taniguchi, R.: Real-time human proxy: an avatar-based interaction system. In: CD-ROM Proc. of Int. Conf. on Multimedia and Expo. (2004)Google Scholar
- 2.Mori, A., Uchida, S., Kurazume, R., Taniguchi, R., Hasegawa, T., Sakoe, H.: Early recognition of gestures. In: Proc. of 11th Korea-Japan Joint Workshop on Frontiers of Computer Vision, pp. 80–85 (2005)Google Scholar
- 3.Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In: Proc. of the 2nd Workshop on Temporal Data Mining, pp. 53–68 (2002)Google Scholar
- 4.Chohen, I., Tian, Q., Zhou, X.S., Huang, T.S.: Feature selection using principal feature analysis. In: Int. Conf. on Image Processing (2002), http://citeseer.ist.psu.edu/cohen02feature.html
- 5.Grünwald, P.: A tutorial introduction to the minimum description length principle. In: Grünwald, P., Myung, I.J., Pitt, M. (eds.) Advances in Minimum Description Length: Theory and Applications, MIT Press, Cambridge (2005)Google Scholar
- 7.Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series with implications for streaming algorithms. In: Proc. of 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2–11 (2003)Google Scholar