On The Pattern Recognition For The Intelligent Bionic Arm Based On Multiple Sensors Information

In the study on the pattern recognition of the surface electromyography (sEMG) fromthe human forearm muscles, we make it a priority to find the pattern recognition methodfor identifying the hand common movements in daily life and emotional expressiongestures, in addition, the study can provide much more detailed information of movementfor the control module of the intelligent bionic arm, such as grip strength and joint angle,so as to assist in completing the activities of daily life better and improving how well theequipment mimic the humankind. In view of the above ideas, the fusion concept with thesEMG signal, the acceleration signal and the grip strength signal is proposed in this paperto help generate a more accurate classification strategy in the training phase of patternrecognition, and expect to promote the development of the pattern recognition module ofthe intelligent bionic arm. In order to undertake the research into the pattern recognitionfor the intelligent bionic arm based on multiple sensors information, more details of themotion will be extracted from the sEMG for the bionic control of the intelligent bionicarm. This paper mainly falls into the following aspects：Firstly, a fusion feature extraction algorithm is proposed in this paper. In the patternrecognition of the sEMG for hand movements, a partial loss of accuracy is caused by thereverse sequence of the channels in the process of the signal acquisition. In order to solvethis problem, a modulus feature extraction algorithm based on the concept of plural isproposed in this paper. The algorithm takes the independent characteristics from dualchannels as the real part and the imaginary part of the plural, and this modulus value ofthe dual characteristics is calculated as the signal feature in this algorithm.The modulusvalue can comprehensively reflect dual channels features,and be insensitive to the channelorder, but the robustness is quite powerful. Due to the high costs of the labeled samplesof sEMG, in the condition of a small number labeled samples, a safe semi-supervised support machine method (S4VM) is applied to carry out the pattern recognition. S4VMtry to exploit the candidate low-density separators simultaneously to reduce the risk ofidentifying a poor separator with unlabeled data. overall performance of S4VM are highlycompetitive to semi-supervised support machine (S3VM), while in contrast to S3VM,S4VM are significantly inferior to inductive supervised support machine. The results ofcomprehensive experiments show that the semi-supervised method with the modulusfeature extraction algorithm proposed in this paper is suitable for the pattern recognitionof sEMG with the limited labeled samples.Secondly, An analogous active segment detection method is proposed in this paper.In the experiment of the sEMG-joint angle pattern recognition, the classical activedetection method which is widely used in the sEMG-movement type pattern recognitionhas already been unsatisfactory, so an analogous active segment detection method isproposed in this paper. It can be applied to accurately locate and find the active segmentaccording the joint angle label by fusing the acceleration signal so as to provide the moreaccurate training data for the neural network to improve its recognition performance. Theexperimental result show that, the analogous active segment detection method can exert aquite good effect on signal segment, and the neural network, which is trained by the datafrom analogous active segment detection method, can be at a higher accurate recognitionrate.Thirdly, two new bionic sEMG feature extraction methods are proposed in this paper.This paper drew inspiration from physiological process of muscle force, aiming foraccurate estimation of muscle force through the features extracted from sEMG. A sEMG-muscle force pattern recognition method of upper arm based on bionic concept ofkinesiology is proposed in this paper. The proposed sEMG feature extraction method ofWSE and WK in this paper is different from that in the past which only cut a small part ofa long sEMG signal. This method tracked the sEMG signal during the time throughbuilding a continuous and non-overlapping window, and made the feature follow the time-variation characteristic of sEMG signal to express its change better. The amputates’residual limb couldn’t provide full training data for pattern recognition, therefore, asolution was investigated in this paper that used the neural network trained by right hand’sdata to predict the relation between the features of sEMG and muscle force of left hand.The contralateral performance is much closer to the ipsilateral performance. This schemeprovided unilateral transradial amputees a possibility to train the intelligent bionic limb byusing their own sEMG. The prediction results of both ipsilateral and contralateral experiment show that the new feature extraction and pattern recognition method forsEMG-Muscle force pattern recognition basing on window sample entropy and windowkurtosis is feasible. The method may offer helpful clues to enhance the performance inpattern recognition module for intuitive control development of intelligent bionic hand.Fourthly, a model for intelligent prosthetic hand is designed by ADAMS in this paper,which has a high fidelity simulation of humanoid shape. The proposed model has16degrees of freedom. Kinematic simulation of prosthetic hand model using ADAMS hasbeen presented in this paper. Several movements have been carried out to verify thereasonability of design of the modeling. Simulation results show that virtual prosthetichand model created in ADAMS can represent the actual prosthetic hand. In order to realizethe debugging function of the virtual prototype model on pattern recognition algorithmand control algorithm, a virtual online co-simulation experiment is conducted on thevirtual prototype model with ADAMS and MATLAB/SIMULINK. Such functionspossessed by Intelligent prosthetic hand as signal acquisition, signal processing, patternrecognition and action control are combined together during this experiment. The controlsignal for the PID algorithm in the SIMULINK is provided by pattern recognition of sEMG.Experimental results show that, the intelligent bionic arm virtual prototype modelproposed in this paper can realize anthropomorphic hand movements under the reasonablecontrol algorithm and the accurate pattern recognition results.Finally, the main content of this dissertation is summarized, and the further researchwill be continued.