Design And Research On Control System Of Lower-Limb Rehabilitation Robot

In recent years,with the increase of the aging population in China,the number of patients with lower extremity motor dysfunction caused by stroke and spinal cord injury has also shown a sharp increasing trend,the walking obstacle has seriously affected the patient's life and work.The traditional artificial rehabilitation walking training method has some problems such as complicated process,low training efficiency and long recovery period,and the autonomous rehabilitation walking training mode assisted by robot is recognized as the best training mode.In order to solve the problem of traditional artificial rehabilitation walking training method and realize self-rehabilitation walking training,this paper studies the control system of lower limb rehabilitation robot according to the signal feedback principle of Surface electromyography(SEMG).Firstly,the basic theories of human kinematics,wavelet mode maximal filter,initial walking time judgment based on threshold method and lower limb motion classification based on Support Vector Machine(SVM)are discussed in this paper;Secondly,according to the specific needs of autonomous rehabilitation walking training,the overall control design scheme is put forward;Then,the hardware modules such as power circuit,signal acquisition,handle control and motor drive are designed based on STM32 single-chip microcomputer;and then the software modules such as signal preprocessing,feature extraction,walking initial time judgment,lower limb walking action classification and drive coordination control are designed;Finally,the control system of the lower extremity rehabilitation robot based on surface electromyography(SEMG)signal feedback is designed,and the coordination of lower limb rehabilitation robot and lower limb movement is realized.In the experimental verification phase,in this paper,five participants were selected to participate in the system test experiment,through the training and testing of lower limb movement samples,the system recognition rate can reach more than 90%,which meets the design requirements.At the same time,the system can collect the SEMG signal,output the recognition result,control all kinds of objects in real time,improve the practicability of the system,and provide a reliable method of motion information capture for the research of Rehabilitation engineering,sports medicine and human-computer interaction.