Saeed Mouloodi
Papers
1
Total Citations
15
H-Index
1
About
Saeed Mouloodi is a researcher at the intersection of biomechanics, machine learning, and equine science. His work focuses on developing deep learning algorithms to predict mechanical strain from motion data, with a particular emphasis on equine hoof biomechanics during exercise. In his most-cited study, Mouloodi demonstrated how neural networks can infer internal tissue strain from external inertial measurements—linear acceleration and angular rates—recorded from a horse’s hoof. This contribution bridges the gap between non-invasive motion capture and internal mechanical loading, offering a powerful tool for injury prevention and performance analysis in veterinary and sports science. With 15 citations to this key paper, his work is gaining traction among biomechanists and animal scientists alike. Mouloodi’s research exemplifies how modern AI can transform traditional biomechanical analysis, making it more accessible and practical for real-world applications. His innovative approach holds promise for advancing both equine welfare and the broader field of musculoskeletal biomechanics.
Research Focus
Key Achievements
Top Papers
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