Device and computer-readable storage medium for identifying Parkinson's disease with gait disorder

By extracting and fusing the full amount of pressure data in gait measurement, including local and global spatial features, and performing multi-scale feature extraction, the existing technology solves the problem of difficulty in early identification and monitoring of gait disorders in patients with Parkinson's disease, and improves Recognition accuracy.

CN118902435BActive Publication Date: 2025-01-17BEIJING FRIENDSHIP HOSPITAL CAPITAL MEDICAL UNIV
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Patent Information

Application Number
CN202411400205.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-10-09
Publication Date
2025-01-17
Estimated Expiration
2044-10-09

AI Technical Summary

Technical Problem

The prior art is difficult to identify and monitor gait disorders in patients with Parkinson's disease early, especially in a short period of time when it is unable to capture subtle gait changes, affecting accuracy.

Method used

By using the full amount of pressure data in gait measurement, the first model and the second model are used to extract the local spatial features and global spatial features of gait respectively, and perform feature fusion. Then the third model is used to extract multi-scale features to obtain the gait. The spatiotemporal characteristics of the state to identify Parkinson's disease with gait disorder.

Benefits of technology

This method can more comprehensively extract rich gait information from the full amount of pressure data and improve the accuracy of identifying Parkinson's disease associated with gait disorder.

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Abstract

The present application discloses a device for identifying Parkinson's disease with gait disorder and a computer-readable storage medium. The device includes: a processor; a memory, in which program instructions for identifying Parkinson's disease with gait disorder are stored. When the program instructions are executed by the processor, the device implements the following operations: obtaining original input data, wherein the original input data at least includes full pressure data in gait measurement; based on the full pressure data, using a first model and a second model to extract local spatial features and global spatial features of gait respectively; performing feature fusion on the local spatial features and the global spatial features to obtain a fusion result; and based on the fusion result and the original input data, using a third model to perform multi-scale feature extraction to obtain the spatiotemporal features of gait to identify Parkinson's disease with gait disorder. The scheme of the present application can improve the accuracy of identifying Parkinson's disease with gait disorder.
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Citation Information

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