Method for recognizing upper limb and hand rehabilitation training action of stroke patient

Abstract

The invention discloses a method for recognizing upper limb and hand rehabilitation training action of a stroke patient. The method comprises the following steps: performing blind source separation onelectromyographic signal data by using a non-negative matrix factorization model, and removing non-stationary muscle activation information to obtain a stable time-varying blind source separation result; performing further pattern recognition by using the factorized time-varying blind source separation result data to improve the stability and accuracy of recognition; and making learning featuresmaintain time and space characteristics at the same time through a CNN-RNN model. The CNN-RNN model does not require manual data feature extraction and screening, directly processes the data, automatically extracts the features and completes classification recognition, can realize end-to-end rehabilitation training action recognition analysis, and is combined with an attention layer for attentionweighting of a hidden state of a second layer in a two-layer two-way GRU layer to give a greater weight to data with a greater contribution degree, so that the data can play a greater role, and the accuracy of classification recognition is further improved.

Description

technical field [0001] The invention belongs to the field of machine learning, and in particular relates to an action recognition method for rehabilitation training of upper limbs and hands of stroke patients. Background technique [0002] Rehabilitation training is an important treatment method in rehabilitation medicine. It mainly uses different exercise training to improve the functional movement disorders of the corresponding limbs of the patient, and restore the patient's motor function as much as possible to achieve the therapeutic effect. Among patients with limb dysfunction caused by stroke, 80% suffer from upper limb dysfunction. Among patients with upper limb dysfunction, only 30% of patients can finally achieve upper limb function recovery, and 12% of patients have better recovery of hand function. . Functional rehabilitation of upper limbs and hands has a profound impact on the quality of life and social participation of stroke patients. The recognition of reha...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the above-mentioned deficiencies in the prior art, the invention provides a recognition method for stroke patients’ upper limb and hand rehabilitation training actions, which solves the problem of information redundancy and loss of time-varying information caused by manual extraction and setting of features, as well as poor recognition performance of machine learning. The problem

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