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

Abstract

The invention provides a virtual reality rehabilitation training method and system based on surface myoelectricity information and depth image information for forearm amputation patients. The method comprises: collecting residual limb myoelectricity information of an amputation patient, and obtaining a palm action category corresponding to the myoelectricity information through a trained gesture recognition model; meanwhile, acquiring the stump pose information of the amputation patient through a depth camera, and identifying the arm action category following the palm action based on the poseinformation; and finally, based on the palm action category and the arm action category, generating a control instruction for controlling a virtual hand action in a virtual environment so as to assistrehabilitation training. According to the method and the corresponding system, the surface myoelectricity information and the depth image information can be fused, the rehabilitation training effectand efficiency of the forearm amputation patient are improved, and the patient can be helped to adapt to the artificial limb 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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