Study On SEMG Based Exoskeletal Robot For Upper Limbs Rehabilitation

As an important branch of medical robot, rehabilitation training robot for hemiplegic upper limbs is a hot research topic both at home and abroad. Based on motor relearning programme, it combines many technology fields such as rehabilitation medicine, human anatomy, mechanics, computer science, and robotics, etc. Different from traditional industrial robots, rehabilitation robots work for human bodies, especially for diseased ones, to assist or displace therapists to carry out rehabilitation exercises. Meanwhile, on the basis of basic functions, human safety, feasibility of clinic operating, system efficiency as well as acceptability and active participation of the patients must be taken into consideration while designing and controlling rehabilitation robots. According to clinic application demands, this dissertation makes researches on system design, surface electromyogram control, force control of rehabilitation training robot for hemiplegic upper limbs and rehabilitation training strategies supported by Heilongjiang Key Science and Technology Project, and lies the theoretical and technical foundation for rehabilitation robot design and training.Taking safety, validity, practicability and comfort into consideration, the design rules for mechanism, control and sensor of rehabilitation for upper limbs is proposed on the basis of human upper limb anatomy and motion mechanism. Under the guidance of rules, a novel 5 DOF exoskeletal upper limb rehabilitation robot for patients in various heights, disable side, impaired degree is developed. The mechanical structure, drive hardware, sensor and kinematics of rehabilitation is described in detail.Surface electromyogramm signal contains motion condition information as the electric signal generated with nerve-muscle motion. It is introduced into rehabilitation robot system to recognize upper limb motions for extracting human active motion intention. Combining with Principal component analysis and multi-features fusion, subsection auto-regressive model method for sEMG signal was proposed to extract four channels sEMG features base on short time stationary hypothesis firstly. The classification test experiment of single BP neural network with different training sample set proves its stronger splittability than traditional AR model method in nonlinear feature space. Refer to instability of single BP neural network classifier, adaptive weighted motion mode ensemble classification algorithm according to output information was proposed based on Adaboost ensemble classification concept. Experiments showed that ensemble classifier has greater generalization than single ones by integrating complement information between each single ones. It can improve judging accuracy of upper limb motion intention for rehabilitation robot.Upper limb active motions have great promotion for rehabilitation course of hemiplegic patients. Joint torque is the direct reflection of active motion intention. So, active rehabilitation training method for upper limbs based on joint torque is studied on the basis of impedance control theory. Static model of rehabilitation robot for upper limbs is built and preprocessing method for joint torque voltage and no load torque removal method are discussed. Then, two active training methods-"damping"and"springing"are proposed base of damping control strategy and joint stiffness control strategy respectively. The former modeled the human-machine interaction as mechanical damping. Rehabilitation robot follows patients'active motion and provides needed resistance with velocity damp. In"springing"anti-resistance training, rehabilitation robot provides rigid resistance for upper limbs with pattern of spring. Patients fell like pull different virtual spring by setting rigid coefficient.The stepping and interactive rehabilitation strategy for upper limbs of hemiplegic patients is proposed. sEMG based self passive rehabilitation exercises is for early recovery stage. The disable limb is controlled by healthy one for harmonious mirror image exercises of all two. Active rehabilitation exercises based on torque signal is for middle to late period. At last experiments are carried on with healthy people as trails on the flat of 5DOF exoskeletal rehabilitation robot for upper limbs. The result of experiments verifies that developed rehabilitation robot can satisfy clinical rehabilitation by various rehabilitation exercise modes, and validates the control method based on sEMG and force.