Effective Inertial Hand Gesture Recognition Using Particle Filtering Based Trajectory Matching

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.