Bionic hand control system and control method based on myoelectric bracelet

Abstract

The invention provides a bionic hand control system and control method based on a myoelectric bracelet. The bionic hand control system comprises an acquisition module, a control module and an execution module, wherein the acquisition module is used for collecting myoelectric signals of a user and the direction, speed, accelerated speed of a built-in gyroscope and other information and sending thecollected information to the control module through a wireless mode; the control module is used for receiving the signals sent by the acquisition module, processing the signals, extracting feature values, performing feature dimension reduction, classifying and recognizing the signals according to a pre-trained classifier, and sending a corresponding execution instruction to the execution module and a display module according to a classified result; and the execution module is used for receiving and executing an instruction issued by the control module, mapping the instruction to the motion state of each motor and controlling the action of a bionic hand. The bionic hand control system and control method based on the myoelectric bracelet have the advantages that the recognition rate of the system is improved, the program complexity is reduced, the real-time performance of the system is improved, accurate motion control can be made on artificial limbs for different users through a self-learning error-correcting mechanism, and the user experience is improved.

Description

technical field [0001] The invention relates to the field of myoelectric prosthesis, in particular to a bionic hand control system and control method based on a myoelectric bracelet. Background technique [0002] According to the 2006 National Bureau of Statistics sample survey data on my country's disabled population, among the various types of disabled people in the country, the physically disabled population is the largest, accounting for 24.12 million people, accounting for 29.07% of the total disabled population. Due to accidents, birth defects, diseases, natural disasters and other reasons, the number of people with physical disabilities is still increasing. Therefore, there is a huge market for prosthetic hands. At present, the domestically developed myoelectric prosthetic hands are still based on a single action mode, and the high-end myoelectric prosthetic hands with multiple action modes mainly rely on imports. Moreover, most existing prosthetic hands have the di...

AI Technical Summary

Problems solved by technology

The scheme does not consider the accuracy of recognition enough

Moreover, the computational load of the neural network classifier is too large, which is not conducive to the real-time performance of the system.

At the same time, only using myoelectric signals for control, the effect is not very good so far

In addition, most of the existing prosthetic control systems use factory settings. In the control system, the corresponding relationship between the myoelectric signal and the action command to be executed is established. The electrical signal is different from the preset myoelectric signal corresponding to the action in the control system, which will cause the control system to issue wrong control commands to the prosthesis, so that the action of the prosthesis cannot truly reflect the user's intention and affect the user experience

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