Comparative Analysis of Tail Tip and Hip Based Features for Dog Tail Wag Classification with Depth Cameras and Support Vector Machines

Understanding dogs’ emotional patterns through their tail movement is key to strengthening the human-canine bond and improving their well-being. This study presents an innovative approach to identify the direction of tail movement (either left or right), using the dog’s hip as the primary reference point. Through a preprocessing and feature extraction process, a Support Vector Machine (SVM) classifier was trained with spatial data from the hip and compared to a classifier from a previous study. The results indicate that the classifier trained with hip data achieved a 99% score in accuracy, precision, and F1-score metrics. Additionally, the Friedman test was performed to verify whether there are statistically significant differences between the two classifiers. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1252Dimensions.Open Alex.