Movement Model And Pattern Recognition Studies On SEMG Of Intellignet Bionic Arm

According to the ideas issued by brain, intelligent bionic arm can control the prosthetichand to complete a variety of actions freedomly. The new generation prosthetic hand cansense the external characteristics of the objects, adjust the parameters of the hand systemautomatically and complete proposed action flexibly and reliably. At present, there are lots ofintelligent bionic arm control signals. Surface eletromyographic signal (sEMG) obtain easilyand control directly and naturally which has proven to be an effective control input forpowered upper limb prostheses. The sEMG signal processing quality influences the securityand reliability of control, directly. This paper mainly aims at using sEMG processing toextract more information from the input signals to increase the accuracy of the controllers. Itsmain contents are presented as follows:Looking for the corresponding relationship between muscle and different movementmodes,identify the best position for putting myoelectricity electrode, testing for reasonableactivities section. The relationship between movements and human muscles is complicated,the corresponding function of each muscle is different, and they influence each other. Theposition of electrode might have impact during collecting EMG. It is the premise of EMGsignals processing that accurate collecting EMG signals to reflect muscle movements underdifferent movement patterns correctly. In this paper, the musculus flexor digitorum sublimisand the musculus flexor carpi radialis are chosen as the position for putting electrode throughhuman anatomy and biological medicine, which can reduce the randomness of the signalcollection and guarantee the accuracy of the signal. In this paper, the sEMG of six movementmotions for eight healthy testers are collected using MQ-8sEMG collection system. Thenmoving average method is used to process instantaneous energy of sEMG sequence. Finallythe signal effectively activities are determined through the comparison with threshold. Afterthe above pretreatment process, the effective activities section is detected, which can help thesEMG analysis in the subsequence.Undecimated double density wavelet transform (UDDWT) and double-parameter softthreshold are proposed to meet the requirement of sEMG nondestructinve de-noisnig. Although the hardware system can remove a part of noise, which made the desired signal ofsEMG not be drowned. It is sill existente during the acquisition and signal transmissionprocess, which have a lot of instrumental noise. In view of this situation, the undecimateddouble density wavelet analysis of signal is used in this paper. It has strict shift invariance,which can avoid the artifacts in signal reconstruction. UDDWT has two wavelet functionswhich can get more detail in every scale compared with discrete wavelet transform (DWT).Double-parameter soft threshold method, which has a relatively smooth transition zone, isused for denoising the high frequency of the signal in the signal decomposition. It cansuppress the noise efficiently meanwhile keep useful detail information to the greatest extent.Experiment results show that the proposed methods, which get high signal-to-ratio whileretaining the signal characteristics, are very suitable for multi-channel sEMG of similar handmovements de-noising.Fourier series and FFT-based blind identification methods are proposed to establishsEMG model. sEMG signal modeling has become an important approach to study of EMGsignal characteristics. Now there are four methds are commonly used in sEMG signalmathematical model, which are linear system model, nonstationary model, bipolar model andthe lumped parameter model respectively. The methods mentioned above, usually describethe relationship between EMG and muscle force from the different aspects to provide thetheoretical basis for judging of muscle activity status, have been widely applied in clinicalmedicine, rehabilitation medicine, sports medicine, neurophysiology and ergonomics. Thosemodeling methods are described emphatically from the biomedical aspects of EMG signals,which can help to distinguish the motion fatigue or muscle abnormalities. They cannot reflectto the relationship between EMG signals with different movement patterns, therefore they arerarely used in intelligent bionics. In these circumstances, Fourier series method and FFT blindidentification method are proposed to build EMG model in this paper to explore relationshipbetween signals and different motions. The model coefficients are used as the signalcharacteristics, the method proposed in this paper has good characterization capabilitiesthrough experiments. By using these as the signal features contribute to subsequentmovement pattern recognition.Semi-supervised boosting algorithm is proposed to classify different movement motions.Traditionally, machine learning is categorized as two paradigms i.e. supervised versusunsurpervised learning. Supervised learning finds out a rule for the predictive relationshipbetween input and output from a set of finite examples in the format of input-output pairs,while unsupervised learning seeks a structure of interests underlying a data set. In general, supervised learning requires many training examples to establish a learner of the satisfactorygeneralization capability. The acquisition of training examples is nontrivial for supervisedlearning, which needs to annotate input data with appropriate labels. In sEMG, the labeledinput data is often difficult, expensive, and time-consuming, especially when it has to be donemanually by experts. On the other hand, there is often a massive amount of unlabeled dataavailable. In order to exploit unlabeled data, semi-supervised learning has become a novelparadigm by using a large number of unlabeled points together with a small number oflabeled examples to build a better learner. Most semi-supervised learning algorithms havebeen designed for binary classification, and are extended to multi-class classification byapproaches such as one-against-the-rest. The main shortcoming of these approaches is thatthey are unable to exploit the fact that each example is only assigned to one class. Additionalproblems with extending semi-supervised binary classifiers to multi-class problems includeimbalanced classification and different output scales of different binary classifiers. A newsemi-supervised boosting algorithm is proposed in this paper that directly solves thesemi-supervied multi-class learning problem. Compared to the existing semi-supervisedboosting methods, the proposed algorithm is advantageous in that it exploits bothclassification confidence and similarities among examples when deciding the pseudo-labelsfor unlabeled examples. The experiments show that the proposed algorithm performs betterthan the state-of-the-art boosting algorithms for semi-supervised learning.Active disturbance rejection control (ADRC) develops the kenel of the PID control, andcombines the observer of modern control ideas. It is a new control method, which algorithmis simple and adaptability, and can automatically compensate internal and externaldisturbances. It can be widely used in industrial applications. ADRC is introduced into bionicaritficail arm in this paper. Adams is used to design artificial arm with16DOF. Themathematical model is obtained from the established artificial arm by using kinematics anddynamics analysis. ADRC adjusts the error between reference input and the Adams actualoutput, to form a stable closed loop control, according to the mathematical model motionedabove. The control method does not depent on the system model, and has the strongadaptation, robusness and operability.Finally, the main content of this dissertation is summarized, and the further researchesare discussed.