Scott Gohery
Papers
1
Total Citations
15
H-Index
1
About
Scott Gohery is a researcher at the forefront of applying deep learning to equine biomechanics and motion analysis. His most-cited work, from 2021, demonstrates a novel approach to predicting mechanical strain on a horse’s hoof during exercise by processing linear acceleration and angular rate data through advanced neural networks. This contribution bridges the gap between wearable sensor technology and musculoskeletal health, offering a non-invasive method to monitor and prevent injury in performance horses. With 15 citations, this paper has already influenced subsequent studies in animal locomotion and veterinary sports medicine. Gohery’s research uniquely combines machine learning, sensor engineering, and equine physiology, providing tools that could transform how trainers and veterinarians assess gait and stress in real time. His work is particularly valuable for students and researchers interested in the intersection of AI and animal welfare, as it opens new pathways for predictive diagnostics in both veterinary and human biomechanics.
Research Focus
Key Achievements
Top Papers
- 1