A parkinson gait assessment method and system based on millimeter wave radar signals
By extracting abnormal gait features of Parkinson's patients using millimeter-wave radar arrays and machine learning methods, this approach solves the problem of inaccurate assessment of Parkinson's gait in existing technologies, enabling low-cost and convenient gait assessment and diagnostic assistance.
CN119138886BActive Publication Date: 2025-12-12NANJING UNIV
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Patent Information
- Application Number
- CN202411294434.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-14
- Publication Date
- 2025-12-12
- Estimated Expiration
- 2044-09-14
AI Technical Summary
Technical Problem
Existing millimeter-wave radar gait assessment technology cannot accurately acquire abnormal gait characteristics of Parkinson's patients, and existing equipment is inconvenient for patients to use and cannot be used for long-term assessment.
Method used
The distance spectrum and Doppler spectrum of Parkinson's patients were obtained by using a millimeter-wave radar array. By combining filtering and machine learning methods, abnormal gait features were extracted and UPDRS-III gait scores were evaluated.
Benefits of technology
It enables non-invasive measurement and assessment of gait characteristics in Parkinson's patients, providing quantitative data to assist doctors in diagnosis, and reducing equipment costs and complexity of use.
✦ Generated by Eureka AI based on patent content.
Abstract
The application discloses a Parkinson gait evaluation method and system based on a millimeter wave radar signal. The method comprises the following steps: acquiring a millimeter wave radio frequency signal in an environment, performing data processing on the signal to obtain a distance spectrum of position change of a Parkinson patient's body and a Doppler spectrum reflecting the degree of change of the Parkinson patient's body; extracting a peak value at each moment from the distance spectrum through a filtering method, calculating a cross-correlation coefficient of the Doppler spectrum as an index of gait pattern matching, and dividing the Doppler spectrum according to each step; calculating gait features according to the filtered distance spectrum peak value and the divided Doppler spectrum; inputting the normalized gait features into a trained machine learning model for feature evaluation to obtain a UPDRS-III score of the patient. The application realizes non-invasive measurement and evaluation of Parkinson gait features in a low-cost manner, is convenient to operate, and is accurate in evaluation effect, thereby providing quantitative data and diagnosis reference for doctors.
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