Hand full finger rehabilitation training and evaluation system based on artificial intelligence technology

Abstract

A hand full-finger rehabilitation training and evaluation system based on the artificial intelligence technology is provided with a portable electroencephalogram collecting device, a man-machine interaction interface, an electroencephalogram intelligent decoding module and an intelligent rehabilitation hand device which are connected in sequence, and the system comprises two working modes of a rehabilitation training mode and a rehabilitation effect evaluation mode. In the rehabilitation training mode, display of rehabilitation actions of the user is given, the user carries out motor imagery of the corresponding actions, the corresponding finger joints are driven to move through the air pump according to the imagery, and the corresponding rehabilitation actions are completed. In the rehabilitation effect evaluation mode, the user selects rehabilitation actions and makes corresponding selected finger actions, and rehabilitation effect evaluation is carried out according to the finger actions of the user. By recognizing the action intention of the user, the intelligent rehabilitation hand equipment is driven to make corresponding actions, and the user is assisted in completing all-finger rehabilitation training of the hand. In addition, the user can evaluate the hand rehabilitation effect, a rehabilitation training closed loop is formed, and rehabilitation training is promoted more efficiently.

Description

technical field [0001] The invention relates to a hand rehabilitation device. In particular, it relates to a rehabilitation training and evaluation system for all fingers of the hand based on artificial intelligence technology. Background technique [0002] Surveys show that stroke has become the leading cause of disability among adults in my country. Stroke has the characteristics of high incidence and high disability rate. The base of stroke patients in my country is large, the treatment period is long, and the recovery effect is poor. A stroke can cause damage to part of the brain, leading to loss of control over parts of the body. As an important organ of the human body, the hands play an extremely important role in completing daily activities. Therefore, it is very important to restore hand function in stroke patients. In addition, for patients who have undergone hand surgery, rehabilitation training is also required to restore hand function. Existing studies have ...

AI Technical Summary

Problems solved by technology

[0004] EEG signal data has the characteristics of nonlinearity, complex features, and low signal-to-noise ratio. Traditional manual inspection methods require experienced experts to inspect EEG, while traditional machine learning algorithms require manual extraction of EEG features for analysis. The above methods all require manual inspection, which is not conducive to long-term monitoring, and will be affected by subjective factors and miss important features of EEG signals.

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