Accelerometer Based Gesture Recognition Using Fusion Features and SVM

Abstract

In this paper, a gesture recognition system based on single tri-axis accelerometer mounted on a cell phone is proposed. We present a novel human computer interaction for cell phone through recognizing seventeen complex gestures. A new feature fusion method for gesture recognition based on time-domain and frequency-domain is proposed. First of all, we extract the time-domain features from acceleration data, that is short-time energy. Secondly, we extract the hybrid features which combine Wavelet Packet Decomposition with Fast Fourier Transform. Finally, we fuse these two categories features together and employ the principal component analysis to reduce dimension of fusion features. The Classifier we used is Multi-class Support Vector Machine. The average recognition results of seventeen complex gestures using the proposed fusion feature are 89.89%, which better than previous works. The performance of experimental results show that gesture-based interaction can be used as a novel human computer interaction for mobile device and consumer electronics.