Abstract
Recently, several smart phones are equipped with a 3D-accelerometer that can be used for gesture-based user interface (UI). In order to utilize the gesture UI for the real-time systems with various users, the diversity robust algorithm, yet having low training/recognition complexity, is required. Meantime, dynamic time warping (DTW) has shown good performance on the simple time-series pattern recognition problems. Since DTW is based on the template matching, its processing time and accuracy depend on the number of templates and their quality, respectively. In this paper, an optimized method for online gesture UI of mobile devices is proposed which is based on the DTW and modified k-means clustering algorithm. The templates, estimated by using the modified clustering algorithm, can preserve the time varying attribute while contain diversities of the given training patterns. The proposed method was validated on 20 types of gestures which are designed for the mobile contents browsing. The experimental results showed that the proposed method is suitable to the online mobile UI.
| Original language | English |
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| Title of host publication | Neural Information Processing |
| Subtitle of host publication | Models and Applications - 17th International Conference, ICONIP 2010, Proceedings |
| Pages | 650-657 |
| Number of pages | 8 |
| Edition | PART 2 |
| DOIs | |
| Publication status | Published - 2010 |
| Event | 17th International Conference on Neural Information Processing, ICONIP 2010 - Sydney, NSW, Australia Duration: 2010 Nov 22 → 2010 Nov 25 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 2 |
| Volume | 6444 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 17th International Conference on Neural Information Processing, ICONIP 2010 |
|---|---|
| Country/Territory | Australia |
| City | Sydney, NSW |
| Period | 10/11/22 → 10/11/25 |
Bibliographical note
Funding Information:Acknowledgements. This research was supported by the Converging Research Center Program through the Converging Research Headquarter for Human, Cognition and Environment funded by the Ministry of Education, Science and Technology (2009-0093676). It was also supported by the Original Technology Research Program for Brain Science through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0018948).
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)