Research On Motion Intention Recognition Technology For Lower Limb Rehabilitation Robot

As a research hotspot in the field of rehabilitation medical engineering and robot engineering,the lower limb rehabilitation robot is a frontier and increasingly mature research direction.The lower limb rehabilitation training which is participated and led by the patient's active movement intention can stimulate the compensation of the central nervous system of the patient.Recombination,while enhancing the high degree of human-machine coupling of the lower limb rehabilitation robot,in favor of the recovery of the patients with lower limb motor function.The exploration of the mapping relationship between human interaction information and motion intentions has many challenges for qualitative,quantitative capture and rapid analysis of human motion intentions and accurate mapping to compliant control system inputs.In this paper,the research on the motion intent recognition technology of the lower limb rehabilitation robot is carried out for the above difficulties.The key technologies such as motion modality and gait subphase identification and motion intention prediction are broken.The experimental platform is built to verify the effectiveness of the proposed algorithm,which is based on the intention inference of the lower limb rehabilitation robot active motion control technology foundation.The research content and contributions of this paper are as follows:(1)The experimental system based on external and auxiliary lower limb rehabilitation robot is constructed,including 6-level plantar pressure sensing system,joint angle sensing system and lower limb posture sensing system.The resource allocation and functional configuration of the hardware platform,software platform and data platform are completed.The motion data perception acquisition experiment completes the acquisition,inspection and analysis of the self-built dataset Motion modality&Gait phase.(2)Aiming at the problem of motion mode and gait subphase identification,a combined feature selection algorithm Filter-BC-MFB-SVM is proposed to obtain the feature set that maximizes the classifier's separation degree.The TM-SVM algorithm is designed in combination with the classification task,which improves the efficiency of model prediction stage and weakens the cumulative effect of horizontal error.Finally,the three motion modes of sit-sitting,standing and walking and the accurate classification of four gait sub-phases of CP phase,CD phase,PP phase and SW phase are completed.(3)For the problem of motion intent prediction,the stacking method is used to fuse the predicted values of the joint angle estimation by the multi-prediction models ARIMA,SVR,RF and XGboost.The confidence interval and prediction interval were constructed by statistical interval estimation method.By using the interval constraint rule,the new predicted value is planned,and the trajectory of human lower limb movement at the next moment of the current phase is obtained accurately and quickly.