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Development of an Efficient 3D Gait Analysis System Using Depth Data Encoding and Dynamic Scale Correction
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AI
DOI：10.3807/KJOP.25.0016.png)
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Abstract
En 中文
This study proposes a three-dimensional (3D) gait analysis system designed to overcome the spatial limitations and distance-dependent accuracy degradation of conventional systems by employing an orthogonal arrangement of multiple depth cameras. To improve data efficiency, the system encodes depth information in RGB format and applies k-means clustering to reduce noise. Two-dimensional joint coordinates, extracted using a YOLOv8-pose model, are scale-corrected according to the subject's height and then fused with the corresponding Z-values from the registered depth images to reconstruct 3D coordinates. The proposed approach is significant in that it integrates low-cost hardware with efficient algorithms to provide accurate and convenient gait analysis, particularly in constrained clinical environments.
Keywords:
3D motion analysis
Human pose estimation
Multi-camera system
Journal
K
IF:
0.1
Papers:
13
Citations:
0