A grasping function evaluation and training system

Abstract

The invention discloses a grasping function evaluation and training system, comprising a host interaction module, which is used to guide a user to adjust the grip strength and muscle contraction status in real time to complete a virtual task; a signal acquisition module is used to collect the mechanical signals and muscles of the user's upper limb an electrical signal; a signal storage module for receiving the mechanical signal and myoelectric signal of the signal acquisition module for storage, as well as storing characteristic parameters; a host control module, respectively connected with the host interaction module and the signal storage module, for storing The mechanical signal and myoelectric signal transmitted by the signal acquisition module are calculated and processed to obtain characteristic parameters, and the virtual scene is controlled by the characteristic parameters to perform corresponding game operations to train the motion control function, and finally the characteristic parameters are transmitted to the signal storage module. The grasping function evaluation and training system of the present invention utilizes multiple parameters of electromyographic signals and mechanical signals to perform human-computer interaction to train and evaluate the hand function of stroke patients, thereby improving the patient's hand motor function.

Description

technical field [0001] The invention relates to the medical field, in particular to a grasping function evaluation and training system based on upper limb mechanical signals and myoelectric signals. Background technique [0002] Stroke (stroke) has become the second largest killer of human health, and it has the characteristics of high morbidity, high mortality, and high disability rate. According to statistics, there are 2 million stroke patients in China every year. The incidence of stroke in my country is increasing at a rate of nearly 9% per year. There are currently 7 million survivors of stroke, 4.5 million of whom have lost their labor force to varying degrees and cannot take care of themselves. The disability rate is as high as 75%, and the recurrence rate within 5 years is as high as 41%. Post-stroke patients with hemiplegia have severe impairments in daily performance. Relevant clinical studies have found that stroke patients with hemiplegia recover much faster ...

AI Technical Summary

Problems solved by technology

Traditional upper limb rehabilitation methods include artificial physical therapy, repetitive occupational therapy, forced exercise therapy, and acupuncture in Chinese medicine. This is a long-term process that requires one-on-one manual interaction between the therapist and the patient, and the degree of automation is not high.

To sum up, there are still relatively few comprehensive rehabilitation assessments for the upper limbs of stroke users, especially combined with multimodal and multi-parameter grasping movements, and the obtained assessments are used to guide the rehabilitation process and use bioelectrical signals to carry out comprehensive rehabilitation assessments. The lack of examples of human-computer interaction on game control can not significantly improve the efficiency and effect of rehabilitation training

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