A probability model for recognition of dynamic gesture based on a finger-worn device

  • Authors:

  • Yinghui Zhou;Zixue Cheng;Lei Jing;Junbo Wang

  • Affiliations:

  • Graduate School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu, Fukushima, Japan;School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu, Fukushima, Japan;School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu, Fukushima, Japan;School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu, Fukushima, Japan

  • Venue:
  • AMT'12 Proceedings of the 8th international conference on Active Media Technology
  • Year:
  • 2012

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Abstract

Gesture recognition based on body-worn devices can be used for healthy improvement and life support. Many methods have been proposed for gesture recognition. However, most of them are concerned about recognition accuracy only. In some practical applications, real-time performance of a recognition method on a wearable device is also a key problem. In the paper, we propose a probability model for accurate and real-time recognition of dynamic gestures. The model learns from HMM but difference in the sense that our model builds probability matrices of feature distribution for each observation points instead of each gesture, which reduces the number of probability matrix to improve processing efficiency. A gesture can be recognized by the way of look-up table to search maximum similarity to pre-stored gestures in the matrices. To verify the model, eight kinds of one-stroke finger gestures are taken as the target of recognition. Result shows reasonable recognition accuracy and computational complexity.