Automatic evaluation method of upper limb motion function in stroke based on deep learning

Abstract

The invention discloses an automatic evaluation method of an upper limb motion function in stroke based on deep learning. The method comprises the following steps: collecting inertial sensing data andmyoelectric data of the upper limb motion process of a patient based on a wearable sensor system; performing length normalization and numerical normalization preprocessing on the collected data; respectively inputting the inertial sensing data and myoelectric data into two convolutional neural networks for feature extraction, performing fusing all characteristics to generate a motion function level based on a Brunnstrom scale, and performing iteration on the model parameters based on a reverse direction propagation algorithm to train a deep learning network model; for patients who need to perform upper limb motion function assessment, performing data acquisition and pretreatment, inputting the data into the trained deep learning model to automatically generate the Brunnstrom staging evaluation results of the upper limb motor function for the patient. The automatic evaluation method can be applied in hospital environment, community and home environment, and can improve the accuracy ofautomatic assessment.

Description

technical field [0001] The invention belongs to the technical field of application of wearable devices, in particular, it relates to an automatic evaluation method for stroke upper limb motor function based on deep learning. Background technique [0002] Stroke, also known as stroke or cerebrovascular accident, is one of the leading fatal diseases in the world. There are 12.42 million stroke patients over the age of 40 in my country, with more than 2.7 million new patients each year, and more than 70% of the surviving population have different degrees of motor dysfunction. Clinical studies have shown that rehabilitation is the most effective way to reduce the disability rate of stroke patients, and rehabilitation assessment is an important part of rehabilitation treatment. Through assessment, we can understand the nature and severity of physical dysfunction of patients and provide important information for formulating rehabilitation goals and treatment plans. in accordance ...

AI Technical Summary

Problems solved by technology

[0006] In view of this, the present invention provides an automatic evaluation method for stroke upper limb motor function based on deep learning, aiming at the subjective differences in traditional scale evaluation and the influence of insufficient feature extraction of evaluation methods based on traditional machine learning on evaluation results. , realize the objectification of rehabilitation assessment, and improve the accuracy of assessment

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