Research On Contorl Of Bio-syncretic Walking Assisted Exoskeleton With Emg Signals

With the increasingly serious aging of the world population and the improvement of living conditions and medical conditions,the average life expectancy of the population is also gradually increasing,and the increase of age also makes people's walking ability decline sharply.in order to extend the independent living time of the elderly,it is necessary to develop a device that can help the elderly walk,and the function realization of assisted walking of the assistance equipment for the elderly depends on the control strategy.In view of the shortcomings of the fuzzy control strategy based on the force signal of the biosyncretic walking assistive exoskeleton,this paper proposes a hybrid control strategy that integrates surface EMG signal and force signal.LabVIEW as a graphical programming software,is used for programming and building human-computer interaction interface,and the control strategy was verified with the existing walking-assisted exoskeleton equipment.Firstly,the gait period of human body was analyzed by image method,the deviation of the center of mass in the coronal plane was measured during the walking period,the change of the distance between hip joint and plantar in sagittal plane was also measured to provide data support for the walk-assisted in the later period.The active relationship of lower limb muscle group in gait period was discussed,and the relation between the surface EMG signal of lower limb muscle group and gait time phase during walking period was analyzed.Secondly,the generating mechanism and characteristics of EMG signal are discussed,and the surface EMG signal acquisition system selected in this paper is introduced.The feature extraction and analysis of surface EMG in time domain,frequency domain and frequency domain are carried out,the dimensionality reduction of feature space is carried out by using the correlation coefficient method,and the subsequent sEMG feature space in time domain used for pattern recognition is selected.Thirdly,based on the LabVIEW software,pattern recognition is carried out for the time domain characteristics of the selected sEMG signal,and BP neural network program is written by LabVIEW,the BP neural network is used to train and learn the sample data to verify the feasibility of the neural networ.Based on LabVIEW,a complete set of program of integrates sEMG signal and force signal mixed control is built.Finally,the bio-syncretic walking assisted exoskeleton used in the experiment was introduced,and several experimenters were selected for the experiment.According to the experimental data,the motion parameters controlled by sEMG signal recognition of motion intention were plotted,and compared with the motion parameters without sEMG signal,to verify the feasibility and effectiveness of the hybrid control of sEMG signal and force signal.