Virtual reality rehabilitation training method and system based on surface myoelectricity and depth image

Abstract

The invention provides a virtual reality rehabilitation training method and system based on surface electromyography information and depth image information for forearm amputee patients. The method includes: collecting the residual limb electromyographic information of the amputee patient, and using a trained gesture recognition model to obtain the palm action category corresponding to the electromyographic information; meanwhile, obtaining the residual limb position and posture information of the amputee patient through a depth camera, and based on the The posture information identifies the arm motion category following the palm motion; finally, based on the palm motion category and the arm motion category, a control instruction for controlling the virtual hand motion in the virtual environment is generated to assist rehabilitation training. The method and the corresponding system of the present invention can integrate surface electromyography information and depth image information, improve the effect and efficiency of rehabilitation training for forearm amputee patients, and help patients adapt to prosthetic use as soon as possible.

Description

technical field [0001] The invention relates to the technical field of rehabilitation engineering, in particular to a virtual reality rehabilitation training method and system based on surface electromyography information and depth image information. Background technique [0002] According to statistics, the number of disabled people in mainland China has reached more than 60 million, including about 18 million to 24 million people with upper limb disabilities, and more than 4 million people with forearm amputation. Installing artificial limbs can make up for the psychological trauma of patients to a certain extent and improve the quality of life of patients. Myoelectric signal control prosthesis is a safe and non-invasive control method. For patients with myoelectric prosthetics, specific muscle training at the amputation site can achieve the purpose of activating the muscles at the amputation site, promote blood circulation at the site, and improve muscle quality. It can ...

AI Technical Summary

Problems solved by technology

[0006] There are two problems in the prior art schemes: First, the upper limb rehabilitation training scheme using camera images for limb recognition is mainly designed for rehabilitation patients (such as stroke patients) with complete upper limbs (including hands), while forearm amputation Due to the lack of hand information, the hand characteristics of the patient are not obvious, and the movement intention of the hand of the residual limb cannot be accurately identified through the image of the camera; second, some virtual reality rehabilitation training programs for amputee patients only use EMG signals as the control source, it can only recognize the hand movement intention of the residual limb, and the virtual hand on the computer can only realize simple hand movements such as opening and closing, but cannot follow the residual limb in real time to realize spatial movement (such as translation and rotation, etc.), and cannot complete complex Rehabilitation training tasks limit the application and effect of rehabilitation training

[0007] In short, in the existing virtual reality rehabilitation training programs for forearm amputee patients, due to the lack of accurate recognition of the movement intention, spatial position and movement posture of the residual limb, patients cannot perform complex and effective rehabilitation in the virtual reality environment. train

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