An Integrated Framework for Running Posture Analysis: Fusing UAV Vision with Deep Learning Models
Keywords:
Drone, Pose recognition, Running-form analysis, Motion-form correctionAbstract
With the acceleration of modern life and rising health awareness, running has become one of the most popular forms of exercise due to its simplicity and lack of venue restrictions. However, incorrect running form can easily lead to sports injuries such as knee joint damage and plantar fasciitis. Traditional motion-analysis systems, while accurate, are expensive and require a laboratory environment, making them neither economical nor convenient for ordinary runners. To address these issues, this paper innovatively combines drones with deep learning to achieve running-form correction. A drone equipped with a vision sensor captures real-time video of the runner; the YOLOv8 algorithm performs human-pose recognition, and the MediaPipe Pose model reconstructs 3D poses, enabling low-cost, portable motion analysis outdoors. The system automatically detects abnormal running form and provides real-time, visual correction suggestions via a mobile app, helping runners adjust their posture promptly and prevent injuries. Compared with traditional solutions, the proposed system offers flexible deployment, ease of use, and low cost, providing the general public with professional, scientific running guidance.
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