Mechanical arm movement speed proportional control method based on myoelectricity

Abstract

The invention discloses a mechanical arm movement speed proportional control method based on myoelectricity. The method comprises the steps that firstly, surface electromyogram signals of the extensorcarpi ulnaris and the flexor carpi radialis are acquired through an electromyogram acquisition instrument, movement initial positions and movement end positions are determined as the movement electromyogram signals by means of an energy threshold value method, and the smooth window average power of the signals are extracted; and the original electromyogram signals are subjected to multiscale decomposition by means of a wavelet analysis method, multiscale fuzzy entropy features of the signals are extracted, the multiscale fuzzy entropy features and the average power form a feature vector to beinput into an extensional K nearest neighbour model classifier, hand movements are identified, and meanwhile, through movement speeds of an orthogonal polynomial fitting operator and a mechanical arm, the mechanical arm is finally controlled to complete corresponding movements at corresponding speeds. By adopting the mechanical arm movement speed proportional control method based on the myoelectricity, the naturalness and initiative of human-computer interaction are improved, accordingly, the operation accuracy and convenience are improved, the danger level of operation of the mechanical armis lowered, and the mechanical arm can complete relatively complex and dangerous tasks.

Description

technical field [0001] The invention relates to a method for proportional control of the motion speed of a manipulator, in particular to the proportional control of the motion speed of a manipulator based on myoelectricity. Background technique [0002] Using the Surface ElectromyoGram (sEMG) on the operator's limbs to control the manipulator has the characteristics of natural movement and good bionic performance, and is an ideal control signal source for the human-computer interaction system. Many scholars at home and abroad have conducted research on myoelectric control. Koirala.k from the Worcester Institute of Technology in Massachusetts, USA, used electromyography signals to predict the activity of human sports muscle groups, and applied sEMG signals to estimate the torque of prosthetic joints. Blana D of the Institute of Medical Science and Technology, Keele University, UK, designed a musculoskeletal model for amputee patients, using sEMG signals obtained from the res...

AI Technical Summary

Problems solved by technology

However, when the low-order model fails to achieve the fitting effect and a high-order model needs to be established, the least squares method becomes inaccurate, and the residual sum of squares also changes exponentially with the increase of the model order

At the same time, the solution of the least squares method in the low-order model can usually meet the real-time requirements of the control, and when the matrix inversion or ill-conditioned matrix appears in the high-order model, the time complexity will be greatly increased.

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