Gripping function evaluating and training system

Abstract

The invention discloses a gripping function evaluating and training system. The gripping function evaluating and training system comprises a host interaction module, a signal acquisition module, a signal storage module and a host control module, wherein the host interaction module is used for guiding a user to adjust gripping force and muscle contraction conditions in real time to fulfill a virtual task; the signal acquisition module is used for acquiring mechanical signals and electromyographic signals of the upper limbs of the user; the signal storage module is used for receiving and storing the mechanical signals and the electromyographic signals of the signal acquisition module, and storing characteristic parameters; the host control module is connected with the host interaction module and the signal storage module respectively, and is used for computing and processing the mechanical signals and the electromyographic signals transmitted by the signal acquisition module to obtain the characteristic parameters, controlling a virtual scene to perform corresponding game operation so as to train the motion control function through the characteristic parameters, and finally transmitting the characteristic parameters to the signal storage module. The gripping function evaluating and training system performs human-computer interaction by utilizing multiple parameters such as the electromyographic signals and the mechanical signals, and is used for training and evaluating the hand function of a stroke patient, so that the hand motion function of the patient is improved.

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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