Intelligent artificial limb training method, device, electronic equipment, intelligent artificial limb and medium

Abstract

The present application discloses a training method for an intelligent prosthesis, which includes: acquiring the movement trajectory data of the user's normal limbs within a preset period of time, and collecting the movement trajectory data of the disabled limbs corresponding to the normal limbs within the preset period of time. The myoelectric signal generated when the limbs do mirror movement; the movement trajectory data is mirrored and flipped; the myoelectric signal is associated with the processed movement trajectory data to generate a training sample; a plurality of the training samples input into the learning model of the intelligent prosthesis for training until the training of the learning model of the intelligent prosthesis is completed. The application also discloses a training device, electronic equipment and a computer-readable storage medium. The purpose of this application is to generate a learning model of an intelligent prosthesis with high accuracy, so as to improve the adaptability between the intelligent prosthesis and the user.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular to a training method for an intelligent prosthesis, a training device, electronic equipment, an intelligent prosthesis, and a computer-readable storage medium. Background technique [0002] Compared with traditional prostheses with limited mobility, intelligent prosthetics can receive myoelectric signals from the human brain through the residual muscles, and then generate corresponding actions after being processed by sensors and mechanical systems installed in the prosthetic arm. This not only enables users to Through intelligent prosthetics, more diverse and finer movements can be made, and it is also convenient for users to flexibly control prosthetic activities. [0003] Since the intelligent prosthesis needs to be pre-trained and learned the user's body movements, and the corresponding learning model can be obtained, it can be put into use on the b...

AI Technical Summary

Problems solved by technology

However, due to the physical differences between different people, even if a normal person with similar physical conditions as the disabled user is found to train the smart prosthesis, the resulting smart prosthesis may still be difficult to accurately adapt to the disabled user

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