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A marker-less human motion analysis system for motion-based biomarker identification and quantification in knee
Kai Armstrong1, Lei Zhang1, Yan Wen1
1Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, United Kingdom.
Frontiers in Digital Health
|February 22, 2024
Summary
We developed an automated method using RGB cameras to identify and quantify biomarkers from patient movement, aiding in osteoarthritis monitoring and treatment assessment. This approach offers a cost-effective and sensitive alternative to traditional motion capture techniques.
Area of Science:
- Biomedical Engineering
- Computer Vision
- Medical Diagnostics
Background:
- Healthcare systems face challenges managing patient flow, particularly for conditions like osteoarthritis (OA).
- Traditional motion capture methods are expensive and complex, limiting their widespread clinical use.
- There is a need for accessible, sensitive tools to monitor disease progression and treatment effectiveness.
Purpose of the Study:
- To propose and validate a novel method for automated biomarker identification and quantification using standard RGB video cameras.
- To enable objective monitoring of treatment response and disease progression in patients, such as those with suspected OA.
- To provide a cost-effective and sensitive alternative to traditional biomechanical analysis techniques.
Main Methods:
- Utilizing deep neural networks with adversarial training and self-attention mechanisms to generate 3D human shape and pose from 2D video data.
- Employing Principal Component Analysis (PCA) for biomarker identification and feature extraction from motion data.
- Automating the generation of clinical reports based on identified biomarkers.
Main Results:
- Validated the capability of standard RGB cameras to capture clinically relevant motion data in a clinical setting.
- Identified statistically significant biomarkers, including cumulative elbow acceleration during sit-to-stand and knee/elbow flexion/extension smoothness during squats and sit-to-stands.
- Demonstrated the potential of these biomarkers to assess treatment success and monitor disease progression.
Conclusions:
- The proposed automated method effectively uses RGB video analysis for objective biomechanical assessment.
- This approach provides a sensitive, low-cost solution for monitoring osteoarthritis and other musculoskeletal conditions.
- The validated biomarkers offer a promising tool for personalized rehabilitation and clinical decision-making.
