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

A technology of motor function and deep learning, applied in medical science, sensors, diagnostic recording/measurement, etc., can solve problems such as insufficient feature extraction, achieve full feature extraction, improve accuracy, and avoid dependence

Inactive Publication Date: 2019-06-14
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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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 i

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

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[0093] Example 1

[0094] After the above steps S1 and S2, 200 IMU and sEMG signal samples are collected, a deep learning model is constructed through S3 and the training process is completed to obtain the automatic evaluation deep learning model H. For subject A whose upper limb motor function level is unknown, the sensor signals of the shoulder touching process are collected through steps S1 and S2, Figure 4 (a) is a schematic diagram of the three-axis acceleration signal of the forearm IMU, Figure 4 (b) Schematic diagram of the sEMG signal of the biceps brachii. Input all the two signals collected into H, and the Brunnstrom staging result of A’s upper limb motor function is output as VI. Since A is a healthy subject in this example, the model automatically evaluates patient A correctly. There is no physician involved in the process, and only the subject himself or a family member assists in completing the sensor binding work.

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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 ...

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Application Information

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IPC IPC(8): A61B5/11A61B5/0488
Inventor 李巧勤刘朗陈智杨尚明刘勇国
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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