Action assisting device based on brain plasticity and control method and circuit thereof

Abstract

The invention relates to the technical field of human-computer interaction, in particular to an action assisting device based on brain plasticity and a control method and circuit thereof. A driving mechanism adopts a linear driving design, so that the driving mechanism and an executing mechanism can be separately placed, namely, the driving mechanism can be placed on the arms, waist or pockets ofa user and even can be placed in a backpack carried on the back, so that the whole equipment is light in weight and convenient to wear; in addition, a classifier, a voter, threshold comparison and self-adaptive control are combined together, which overcomes the difficulty of inaccurate recognition of motion intention by electromyographic signal, and reduces the control delay of the entire system,so that the motion intention of the user can be directly reflected; and in the control of the executing mechanism, a self-adaptive compliance control mode is adopted, main power estimation of the electromyographic signal is combined, power can be provided as required, and therefore better human-computer interaction experience and rehabilitation effect can be achieved.

Description

technical field [0001] The invention relates to the technical field of human-computer interaction, in particular to a brain plasticity-based motion assisting device, a control method and a circuit thereof. Background technique [0002] Movement assistance equipment is mainly aimed at people with limb movement disorders, and has been widely used in neurological rehabilitation, medical and military fields. Since the use of hands is extremely important and frequent in daily life, the design of assistive devices for hand movements is particularly important. [0003] The patients with hand dysfunction can be mainly classified into two categories, one is caused by damage to the brain and neural pathways, and the other is caused by muscle damage. For the first type of patients, it can be treated by nerve transfer surgery; for the second type of patients, it can be treated by targeted muscle nerve transplantation. However, whether it is nerve transfer or targeted nerve transplanta...

AI Technical Summary

Problems solved by technology

The collection of mechanical information is convenient and the signal is stable, but it can only be obtained after the user starts exercising, and there is a serious lag. At the same time, some small hand movements cannot collect mechanical information, and the mechanical information cannot directly reflect the user's movement. intention

[0007] Motion intention recognition based on bioelectrical signals can solve the problem of hysteresis very well, but since the electromyography signal will have interference signals (such as superimposed signals, noise signals, etc.), the accuracy of recognition is low, which will affect the judgment of motion intention to mislead

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