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Published on: January 12, 2024
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[A two-dimensional video based quantification method and clinical application research of motion disorders]
Yubo Sun1,2, Peipei Liu3, Yuchen Yang1,2
1College of Artificial Intelligence, Nankai University, Tianjin 300350, P. R. China.
Summary
This study developed a smartphone-based method to analyze gait disturbance in neurological disorders like Parkinson's disease. It achieved 91% accuracy in classifying patients, offering a convenient telemedicine solution.
Area of Science:
- Neurology
- Biomedical Engineering
- Computer Vision
Context:
- Aging population and uneven medical resources increase demand for telemedicine.
- Gait disturbance is a key symptom of neurological disorders, including Parkinson's disease (PD).
- Accurate, accessible gait analysis is crucial for remote patient monitoring.
Purpose:
- To propose a novel, quantitative approach for assessing gait disturbance using smartphone-captured 2D videos.
- To develop and validate a system for extracting gait features and classifying neurological movement disorders.
- To provide an objective and intelligent solution for telemedicine in neurology.
Summary:
- A convolutional pose machine extracts human body joints from 2D videos.
- Gait phase segmentation and upper/lower limb feature extraction are performed.
- A height ratio-based method enhances spatial feature capture, achieving <3 cm step length error.
- Clinical validation with 64 Parkinson's disease patients and 46 controls showed 91% classification accuracy using random forest.
Impact:
- Enables objective, convenient, and intelligent telemedicine for neurological movement disorders.
- Facilitates remote monitoring and early detection of gait abnormalities.
- Potential to improve healthcare accessibility for aging populations and those in remote areas.
