Multi-dimensional surface myoelectricity signal artificial hand control method based on principal component analysis

Abstract

The invention discloses a multi-dimensional surface myoelectricity signal artificial hand control method based on a principal component analysis. The control method comprises the follow steps that 1, an arm ring provided with a 24-channel array myoelectricity sensor is worn to a front arm of a testee, and five finger joint posture sensors are worn at the distal phalanx of a thumb of the subject and at the middle phalanxes of rest fingers of the testee respectively; 2, the testee conducts a five-finger independent bending and stretching training, and meanwhile, the myoelectricity sensing array data and the data of the finger joint posture sensors are collected; 3, the principal component analysis is used for decoupling the myoelectricity sensing data to form a finger movement training set; 4, the sensors worn on the fingers are removed after the training is finished; 5, data fitting is performed on the finger movement training set by adopting a neural network method, and a finger continuous motion prediction model is constructed; and 6, current bending angles of the fingers are predicted through the finger continuous motion model. According to the control method, the non-consistency of the discrete action modal classification can be overcome, and finally smooth control on an artificial hand is achieved.

Description

technical field [0001] The invention relates to a control method for a manipulator, in particular to a method for controlling a prosthetic hand based on a principal component analysis method with multi-dimensional surface electromyography signals. Background technique [0002] Biomechatronics dexterously operated prosthesis is an intelligent interactive device that can work with the environment, humans and other robots. It recognizes the operator's action intentions by collecting bioelectrical signals from the human body. Research on artificial limbs can drive technological innovation in the field of functional reconstruction and rehabilitation engineering for the disabled, and extend and develop the scientific connotation of equipment manufacturing. Its scientific and technological achievements can be radiated and applied to high-end medical equipment, mechanical and electrical integration intelligent robots, hazardous environment exploration and disaster rescue equipment, ...

AI Technical Summary

Problems solved by technology

[0004] Due to the different degrees of forearm muscle development and operating habits of different subjects, it is often difficult to extract universal motion rules for all individuals.

[0005] Human finger movements are very complex. Existing research focuses on the recognition of isolated gestures, and rarely recognizes continuous movements of gestures.

This technology uses the principal component analysis method to decouple the complex muscle activities of the hand, and can analyze the continuous activities of each finger. At present, there is no literature on the continuous motion estimation of fingers.

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