SEMG Signal Processing Base On Histogram And Spectrum

Electromyography signal contains plenty information connected with body movements. It is a main task that controlling the muelectric limb by using the surface electromyography signals. So it is very usful to the research of the myelectric limb that recovering the information from the SEMG signal. According to the requirement of the research subject,the paper designs the control system of the myoelectric prosthesis based on the SEMG signal. For this, many theory exploration and practice will be done on collection of SEMG signal, feature extraction, pattern recognition of hand movements and the control of myoelectric limb. The author do some deeply research on Blind Source Separation , the feature extract by the histgram and the frequency analysis, and the classification. In order to implement the goal, the paper makes following work and innovations:(1) Firstly, the paper briefly generalizes the research background, current research situation and research significance of myoelectric prosthesis. Secondly, the paper summarizes methods of feature extraction used in SEMG signal processing, and establish the method of feature extraction is based on the analyzing to the histgram and the frequency. And the last introduce the method of pattern recognition applied in myoelectric limb.(2) In order to eliminate the signal retraction of multi-channel Surface Electromyography (SEMG), this paper proposes a new separation method based on the referenced cumulant on the time-frequency. The method combined the merits of them. First construct the time-scale cumulant matrice, and then do the time-frequency analysis, at last, the estimation of the SEMG can be performed by the non-orthogonal joint diagonalization. It is a good base for the next patten-recgonization.(3) In order to extract effectively the feature of SEMG signal, the paper firstly offers a method of feature extraction based on the histogram of the SEMG and the Time-frequency analysis. In order to token the features of SEMG signal correctly, the Feature Parameter need to be extracted from different aspects. So the paper extracted the amplitude ratio and the power spectrum ratio from the time domain and frequency domain. At last, we used the BP neural network to recognize the patten respectively and get the final result from the the D-S evidence theory. This method use the D-S evidence theory to deal with the imprecise and vague information and also to play neural network's merits like as self-learning, adaptive and fault-tolerance capabilities, so it is a Robustness'arithmetic,and it can also improve the ratio of the patten recgonization. The method use the different paeampers to supply the more information, and it can avoid the unsure from the one feature parameter ,and also it avoid the unrecgonize because of the simple feature combine. The paper has taken a compare between the result of using a feature character and the two combine, it shows the method that the paper offered has a great advantage, its ratio even reach 94%. Off course, the paper has also another compare from the different processing methods, it also shows that the recognition effect is better from the processing with methods which the paper offered.