Hadi Rahmanpanah
Papers
1
Total Citations
15
H-Index
1
About
Hadi Rahmanpanah is a researcher at the intersection of biomechanics and artificial intelligence, whose work focuses on equine locomotion and the application of deep learning to motion analysis. His major contribution lies in developing novel computational methods to predict mechanical strain on horse hooves during exercise, using data from inertial measurement units (IMUs) that record linear acceleration and angular rates of motion. His most-cited paper, "The use of deep learning algorithms to predict mechanical strain from linear acceleration and angular rates of motion recorded from a horse hoof during exercise" (2021), has garnered 15 citations, demonstrating its impact in the emerging field of non-invasive equine biomechanics. This work is notable for its potential to improve animal welfare by enabling real-time monitoring of hoof health and performance without invasive sensors. Rahmanpanah’s research bridges veterinary science and machine learning, offering a data-driven approach to understanding the mechanical stresses experienced by horses during high-intensity activities. His contributions are particularly relevant for researchers in sports science, veterinary orthopedics, and AI-driven biomechanics, highlighting a promising avenue for preventing injuries in athletic animals.
Research Focus
Key Achievements
Top Papers
- 1