M.W. Jiang

Publications (2)0 Total impact

• Conference Proceeding: EMG Signal Classification for Myoelectric Teleoperating a Dexterous Robot Hand

  J.Z. Wang, R.C. Wang, F. Li, M.W. Jiang, D.W. Jin
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  ABSTRACT: This paper details a strategy of discriminating finger motions using surface electromyography (EMG) signals, which could be applied to teleoperating a dexterous robot hand or controlling the advanced multi-fingered myoelectric prosthesis for hand amputees. Finger motions discrimination is the key problem in this study. Thus the emphasis is put on myoelectric signal processing approaches in this paper. The EMG signal classification system was established based on the surface EMG signals from the subject's forearm. Four pairs of electrodes were attached on the subjects to acquire the signals during six types of finger motions, i.e. thumb extension, thumb flexion, index finger extension, index finger flexion, middle finger extension, and middle finger flexion. In order to distinguish these finger motions. A combination of autoregressive (AR) model and an artificial neural network (ANN) was used in the system. The discrimination procedure consists of two steps. Firstly, the AR model is used to preprocess the surface EMG signals to reduce the scale of the data. These data will be imported into the myoelectric pattern classifier. Secondly the coefficients of AR model are imported into the ANN to identify the finger motions. The experimental results show that the discrimination system works with satisfaction
  Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the; 02/2006
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  Conference Proceeding: A Method of Recognizing Finger Motion Using Wavelet Transform of Surface EMG Signal

  M.W. Jiang, R.C. Wang, J.Z. Wang, D.W. Jin
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  ABSTRACT: In this paper, an identification method of finger motions using the wavelet transform of multi-channel electromyography (EMG) signal is presented. The first step of this method is to analyze surface EMG signal detected from the subject's upper arm using the multi-resolution of wavelet transform, and extract features using the variance, maximum and mean absolute value of the wavelet coefficients. In this way, a new feature space is established by wavelet coefficients. The second step is to import the feature values into an artificial neural network (ANN) to identify the finger motion. Based on the results of experiments, it is concluded that this method is effective in identification of finger motion. Thus, it provides an alternative approach to use the surface EMG in controlling the finger motion of a multi-fingered prosthetic hand
  Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the; 02/2006