Parallel lower limb rehabilitation robot adaptive training control method and rehabilitation robot

Abstract

The invention discloses a self-adaptation training control method of a parallel lower limb rehabilitation robot and the rehabilitation robot. The method comprises the steps that lower limb muscle activity and contractility state are estimated according to collected electromyographic signals and impedance model parameters of the robot are adjusted in a self-adaptation mode according to the lower limb muscle activity and contractility state; meanwhile, interaction acting force between a patient and the robot is obtained, and calculation and correction of the motion of the robot are achieved through a recognition result and an impedance model; a master control portion of the rehabilitation robot provides online trajectory planning and inverse kinematics, control instructions are sent to a controller and a driver of a corresponding joint, and self-adaptation training control over the robot is achieved. Intelligentization can be achieved in the rehabilitation process, and the fundamental practical significance and application value for improving the patient rehabilitation effect and improving the patient rehabilitation initiativeness and motivation are achieved.

Description

Technical field [0001] The invention relates to the field of auxiliary medical rehabilitation robot control, in particular to an adaptive training control method based on muscle state assessment of a parallel lower limb rehabilitation robot. This method collects the surface EMG signals of the lower limbs of the human body and processes them to evaluate the contraction state of the relevant muscles online to adaptively adjust the parameters of the human-machine impedance model, and drive the rehabilitation robot to assist training with different degrees of compliance. The method of the invention is suitable for the fields of medical rehabilitation robots and artificial limb control. Background technique [0002] The rehabilitation of the elderly, the disabled, and people with limb movement disorders caused by stroke, hemiplegia, etc. has become an urgent social problem. The traditional rehabilitation treatment of limb dysfunction mainly relies on one-to-one freehand training by th...

AI Technical Summary

Problems solved by technology

The current rehabilitation training system rarely introduces EMG signals in the overall operation cycle to realize the autonomous, intelligent, and optimal control of the robot. It cannot detect the muscle activity and rehabilitation state of the affected limb in real time during the robot training process, and cannot adapt itself according to the muscle operation state. Modified impedance model lacks the evaluation of muscle activity and contraction of the affected limb, so it is difficult to adapt to different rehabilitation cycles of different patients

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