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.
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References
Dachselt, R., Buchholz, R.: Natural throw and tilt interaction between mobile phones and distant displays. In: Proc. of the 27th Int. Conf. Extended Abstracts on Human Factors in Computing Systems, pp. 3253–3258 (2009)
Liu, J., Wang, Z., Zhong, L., Wickramasuriya, J., Vasudevan, V.: uWave: Accelerometer-based personalized gesture recognition and its applications. Pervasive and Mobile Computing 5(6), 657–675 (2009)
Pylvänäinen, T.: Accelerometer based gesture recognition using continuous HMMs. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3522, pp. 639–646. Springer, Heidelberg (2005)
Kela, J., Korpipaa, P., Mantyjarvi, J., Kallio, S., Savino, G., Jozzo, L., Marca, S.D.: Accelerometer-based gesture control for a design environment. Personal and Ubiquitous Computing 10, 285–299 (2006)
Wu, J., Pan, G., Zhang, D., Qi, G., Li, S.: Gesture recognition with a 3-D accelerometer. In: Zhang, D., Portmann, M., Tan, A.-H., Indulska, J. (eds.) UIC 2009. LNCS, vol. 5585, pp. 25–38. Springer, Heidelberg (2009)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing 26(1), 43–49 (1978)
Salvador, S., Chan, P.: Fast DTW: Toward accurate dynamic time warping in linear time and space. Intelligent Data Analysis 11(5), 561–580 (2007)
Capitani, P., Ciaccia, P.: Warping the time on data streams. Data and Knowledge Engineering 62(3), 438–458 (2007)
Wobbrock, J.O., Wilson, A.D., Li, Y.: Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes. In: Proc. of the 20th Annual ACM Symposium on User interface Software and Technology, vol. 20, pp. 159–168 (2007)
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Choe, B., Min, JK., Cho, SB. (2010). Online Gesture Recognition for User Interface on Accelerometer Built-in Mobile Phones. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_80
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DOI: https://doi.org/10.1007/978-3-642-17534-3_80
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