This paper introduces a novel objective video-based method for assessing canine Attention-Deficit/Hyperactivity Disorder (ADHD)-like behavior using machine learning. The study trained a Random Forest classifier on video recordings of dogs in a veterinary consultation room, analyzing movement features such as total distance and average speed. This method achieved 81% accuracy in distinguishing between dogs with clinically treated ADHD-like behavior and healthy controls. The resulting H-score, which quantifies the degree of ADHD-like behavior, showed a reduction in 8 out of 11 patients following medical treatment. Behavioral experts perceived the H-score as a valuable, objective tool to support clinical assessment and communication with owners, although they emphasized it should not be the sole basis for diagnosis.
Supervised Neural Style Transfer as an Augmentation Technique for Facial Landmark Detection
A major challenge in training accurate facial landmark detection models
