Artificial Intelligent Based Human Motion Pattern Recognition and Prediction for the Surface Electromyographic Signals

In this research, the artificial intelligent method based human motion pattern recognition for surface electromyographic (EMG) signal is proposed. As the EMG signal is a measurement of anatomical and physiological characteristic of the given muscle, the macroscopical movement patterns of the human body can be classified and recognized. By using the technology of wavelet packet transformation, the high-frequency noises can be eliminated effectively and the characteristics of EMG signals can be extracted. Auto-regressive model is adopted to effectively simulate the stochastic and non-stationary time sequences using a series of auto-regressive coefficients with a typical order. Artificial neural network is utilized to distinguish the different force levels in the game of arm wrestling. The efficiency of the proposed methods are proved by experiment results.