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Journal of Electrical and Computer Engineering
Volume 2018 (2018), Article ID 6296013, 9 pages
https://doi.org/10.1155/2018/6296013
Research Article

Effective Inertial Hand Gesture Recognition Using Particle Filtering Based Trajectory Matching

1Chengdu Institute of Computer Applications, Chinese Academy of Science, University of Chinese Academy of Sciences, Chengdu 610041, China
2Guangzhou Electronic Science Inc. of Chinese Academy of Science, Guangzhou, China
3School of Aeronautics and Astronautics and School of Automation, University of Electronic Science and Technology of China (UESTC), Chengdu, China

Correspondence should be addressed to Bin Chen; moc.uhos@603nibnehc and Jin Wu; moc.liamtoh@ctseu_uw_nij

Received 2 May 2017; Revised 24 July 2017; Accepted 23 August 2017; Published 11 February 2018

Academic Editor: Tiancheng Li

Copyright © 2018 Zuocai Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Hand gesture recognition has become more and more popular in applications like intelligent sensing, robot control, smart guidance, and so on. In this paper, an inertial sensor based hand gesture recognition method is proposed. The proposed method obtains the trajectory of the hand by using a position estimator. The proposed method utilizes the attitude estimation to produce velocity and position estimation. A particle filter (PF) is employed to estimate the attitude quaternion from gyroscope, accelerometer, and magnetometer sensors. The improvement is based on the resampling method making the original filter much faster to converge. After smoothing, the trajectory is then converted to low-definition images which are further sent to a backpropagation neural network (BP-NN) based recognizer for matching. Experiments on real-world hardware are carried out to show the effectiveness and uniqueness of the proposed method. Compared with representative methods using accelerometer or vision sensors, the proposed method is proved to be fast, reliable, and accurate.