Online gesture recognition for user interface on accelerometer built-in mobile phones

Bong Whan Choe, Jun Ki Min, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Citations (Scopus)

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 languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publicationModels and Applications - 17th International Conference, ICONIP 2010, Proceedings
Pages650-657
Number of pages8
EditionPART 2
DOIs
Publication statusPublished - 2010 Dec 21
Event17th International Conference on Neural Information Processing, ICONIP 2010 - Sydney, NSW, Australia
Duration: 2010 Nov 222010 Nov 25

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6444 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on Neural Information Processing, ICONIP 2010
CountryAustralia
CitySydney, NSW
Period10/11/2210/11/25

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Online gesture recognition for user interface on accelerometer built-in mobile phones'. Together they form a unique fingerprint.

  • Cite this

    Choe, B. W., Min, J. K., & Cho, S. B. (2010). Online gesture recognition for user interface on accelerometer built-in mobile phones. In Neural Information Processing: Models and Applications - 17th International Conference, ICONIP 2010, Proceedings (PART 2 ed., pp. 650-657). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6444 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-17534-3_80