Does the tail show when the nose knows? AI-enhanced analysis of tail kinematics outperforms human experts at predicting when detection dogs find their target odor

Detection dogs are utilized for searching and alerting various substances due to their olfactory abilities. Dog trainers report being able to "predict" such identification based on subtle behavioral changes, such as tail movement. This study investigated tail kinematic patterns of dogs during a detection task, using computer vision to detect tail movement. Eight dogs searched for a target odor on a search wall, alerting to its presence by standing still. Dogs’ detection accuracy, against a distractor odor, was 100% with trained concentration while, during threshold assessment, progressively reached 50%. In the target odor area dogs exhibited a higher left-sided tail-wagging amplitude. The AI model showed a 77% accuracy score in the classification and, in line with the dogs’ performance, progressively decreased at lower odour concentrations. Additionally, we compared the performance of an AI classification model to that of 190 detection dog handlers in determining when a dog was in the vicinity of a target odor.  The AI model outperformed dog professionals, correctly classifying 66% against 46% of videos. These findings indicate the potential of AI-enhanced techniques to reveal new insights into dogs’ behavioral repertoire during odour discrimination.

Methods
This dataset contains processed time-series data of dogs’ tail kinematics during an odour detection task. It was collected as part of a study «Does the tail show when the nose knows? AI-enhanced analysis of tail kinematics outperforms human experts at predicting when detection dogs find their target odor», investigating tail movement patterns when dogs were exposed to a target odour and a distractor odour on a search wall. The dataset includes CSV files with landmark coordinates extracted using computer vision techniques, as well as source code.