An action recognition method for upper limb and hand rehabilitation training of stroke patients

Abstract

The invention discloses an action recognition method for upper limbs and hand rehabilitation training of stroke patients, which adopts a non-negative matrix decomposition model to perform blind source separation on electromyographic signal data, removes non-stationary muscle activation information, and obtains a stable time-varying blind source Separation results; apply the decomposed time-varying blind source separation result data for further pattern recognition to improve the stability and accuracy of recognition; use the CNN-RNN model to keep the learned features at the same time. Time and space characteristics. The CNN-RNN model does not need manual data feature extraction and screening, directly processes data, automatically extracts features and completes classification and recognition. It can realize end-to-end rehabilitation training action recognition and analysis, and combines the attention layer for the first two-layer two-way GRU layer. The hidden state of the second layer is used for attention weighting, giving greater weight to the data with a large contribution, so that it can play a greater role, thereby further improving the accuracy of classification and recognition.

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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