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This paper proposes a gesture recognition system based on single tri-axis accelerometer mounted on a cell phone for human computer interaction (HCI). Three feature extraction methods, namely discrete cosine transform (DCT), fast Fourier transform (FFT) and a hybrid approach which combine wavelet packet decomposition (WPD) with fast Fourier transform are proposed. Recognition of the gestures is performed with support vector machine (SVM). Recognition results are based on acceleration data collect from 67 subjects. The best average recognition result (87.36%) for 17 complex gestures is achieved with wavelet-based method, while DCT and FFT produce accuracy of 85.16% and 86.92% respectively. The performance of experimental results shows that gesture-based interaction can be used as a novel HCI for mobile applications, such as games control and music navigation.