Training method and system for rope traction upper limb rehabilitation robot based on admittance control

Abstract

The invention discloses a training method and system for a rope traction upper limb rehabilitation robot based on admittance control. When a user performs an upper limb joint rehabilitation training exercise, an interaction force signal of the upper limbs of the user applied to the rope traction rehabilitation robot and a kinematic signal of the upper limbs are collected in real time; the interaction force signal is converted into a motion parameter of a desired motion trajectory through an admittance model, and a motion parameter of a target motion trajectory is determined according to the motion parameter of the desired motion trajectory and the kinematic signal of the upper limbs; the determined motion parameter serves as a controlled variable to be converted into a motor control variable of the rope traction rehabilitation robot, and corresponding motor output is controlled to achieve a self-control rehabilitation training movement of the user, thereby increasing the active participation of the user.

Description

technical field [0001] The present invention relates to the technical field of medical rehabilitation, in particular to a training method and system for a rope traction upper limb rehabilitation robot based on admittance control. Background technique [0002] Stroke is characterized by high morbidity, disability and mortality, and is the primary disease that threatens human health. According to statistics, there are 2 million stroke patients in my country every year, and the incidence rate is as high as 1.20 / 100,000. With the aging of my country's population, the incidence rate of stroke is showing an upward trend year by year. 75% of stroke survivors have varying degrees of motor dysfunction, have lost their labor force or are unable to take care of themselves to varying degrees, and 25% of them still cannot use the affected limb freely five years after the stroke; especially the function of the upper limbs Recovery is often slower than that of the lower extremities due to...

AI Technical Summary

Problems solved by technology

Myoelectric control judges the user's movement intention based on the user's myoelectric signal, and the patient's active participation is strong; Professor Ai Qingsong of Wuhan University of Technology conducts pattern recognition on the myoelectric signal, combined with the variable impedance control strategy, and applies it to the 6-degree-of-freedom rehabilitation of the lower limbs Robots; however, this algorithm is more complex and has greater individual differences

[0004] To sum up, although the control methods of rehabilitation robots have made good progress in the accuracy and compliance of trajectory tracking for rehabilitation training, the control and execution are mostly centered on the robot. The mode of exercise has the problem of insufficient active participation of patients

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