Research And Realization On Wrist Force Estimation Based On Muscle Synergy

Surface EMG signal is rich in information related to the nervous system,so it was often uesd as the control signal of the rehabilitation equipment.There exist some drawbacks of traditional control methods based on pattern recognition: Firstly,pattern recognition can only achieve discrete control,but human movement is continuous;the secondly,when the muscles are more involved,the time domain feature extraction often have higher dimensions,and time-domain feature extraction have poor robustness in face of the electrode position shifting.In recent years,some scholars believe that the human central nervous system is not directly control every muscle contraction,but rather a set of control parameters which have smaller number of dimensions,namely muscle synergy model.This makes that feature selecting have practical significance and can also make up for the traditional time-domain feature extraction deficiencies when making use of EMG signals to do pattern recognition or prediction information.Human upper limb engaged in complex and elaborate activity,So in this paper,aiming at above problems,we take the wrist joints as the research object,study the feature extraction method based on muscle synergy analysis.and propose a new weights and thresholds initialization method to improve BP neural network.In the meantime establishing a force prediction model based on motion discriminating,and finally design a software system based on the force forecasting to verify the accuracy and utility of the motion recognition and force prediction.Specific studies including:(1)Acquisition EMG signal of forearm and the force signal of wrist,then signal preprocessing was done(filtering,synchronization,normalization,etc.).To overcome the drawbacks of traditional time-domain feature extraction and motion recognition,we propose the use of feature extraction method based on muscle synergy,using non-negative matrix factorization to determine wrist four movements(flexion,extension,radial deviation,ulnar deviation)synergistic muscles and meanwhile through experiments prove that this feature is more robust than the traditional time-domain feature when electrode position shifting taking place,and reveals the human muscle movement synergism.Through matrix decomposition,we also get the information about the changes of the muscle synergy over time,which can be used as the input of the force estimation model.(2)We design two different structures of the neural network,the NMF coefficient matrix obtained as tht input to the neural network,the output was the corresponding force.We use the Levenberg-Marquard algorithm and adaptive learning rate algorithm and then propose a new methods to achieve initialization weights and thresholds to overcome the inaccurate predictions,slow convergence and easy to fall into local minima problems caused by the weights and thresholds random initialization.Not only the prediction error was reduced,the speed of network convergence was accelerated either.Finally we design a force prediction model based on motion discrimination.On the basis of correct idenfication of the wrist motion,the corresponding force was predicted at the same time.(3)Here we study the force application in the field of robotics rehabilitation.We designe and implement a software system based on the motion determination and force estimation.Combined with the software,we adopt the proportional control stratergy based on force prediction to overcome the deficiency of the traditional control methods baesd on the pattern recognition.The stratergy adjust the device speed according to the force,which realize the continurous control of the rehabilitation device.