Human Gait Modelling via Stationary Wavelet Transform and Radial Basis Function Neural Networks

In this paper, a new method, combined with stationary wavelet transform and Gaussian Radial Basis Function Neural Networks (GRBFNN), is proposed for solving the problem of human gait modelling. Firstly, the hardware system, consisting with MPU6050 sensor, wireless transform module, micro control unit and computer, is designed for collecting the gait signal. Secondly, stationary wavelet transform is applied for decomposing the gait signal with 5 scales. In order to remove the high frequency noise and baseline drift, the coefficients of high frequency and low frequency are set as zero. Thirdly, after wavelet denoising, setting a large enough space to cover the gait signal and establishing lattice points with equal intervals in this space, we take gait signal as input and use lattice points as mapping center in GRBFNN design. Fourthly, the identification equation of continuous dynamical system is rewritten into discrete one, and GRBFNN is used for modelling the dynamical function of gait signal. In order to ensure the stability of iteration, the chosen of gain parameter is proven by the Z transform. Finally, comparing with wavelet neural networks(WNN), the result of test in practice demonstrates the superiority of the proposed method for solving the problem of human gait modelling.