DOI: 10.5061/dryad.dz08kps7r
title: 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

description: 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.
description: 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.
description: # 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 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. ##
 Description of the data and file structure This dataset consists of
 processed numerical data derived from video recordings of dogs
 participating in an odour detection task and a processing code. Video data
 is available upon request. Data Components: 1\. Coded Landmark Data (CSV
 files in a zip archive). • All CSV files containing detected landmark data
 are stored in a compressed archive: split-predictions-conf.zip. • Inside
 the archive, data is organized into a hierarchical folder structure based
 on individual dogs and their names. • Each dog’s folder contains
 subfolders corresponding to different areas of the search wall (Area 1,
 Area 2, and Area 3). • Within each area folder, there is a set of CSV
 files corresponding to different trials. Example folder structure inside
 split-predictions-conf.zip: ``` /split-predictions-conf.zip/ ├── bubba/ │
 ├── AREA_1/ │ │ ├── 06_04_23_bubba_test2_session1_trial1_16_area1.csv │ │
 ├── 06_04_23_bubba_test2_session1_trial1_20_area1.csv │ │ ├── ... │ ├──
 AREA_2/ │ ├── AREA_3/ ├── buster/ ├── ... ``` Each CSV file contains the
 following columns: • Frame Number – Sequential frame index. • Time (s) –
 Timestamp in seconds. • Bounding Box – Coordinates of the bounding box
 around the dog (top-left X, top-left Y, bottom-right X, bottom-right Y). •
 Confidence – Confidence score assigned by the YOLO model for the bounding
 box detection. • Landmarks – X and Y positions for six detected landmarks,
 representing key points on the dog’s body (total of 12 values). 2\.
 Metadata File • info.csv provides a mapping between video names and
 classification labels. • This file is used to determine the class
 (area_target, area_distractor) of a specific dog in a specific area and
 trial. 3\. Code for Processing and Analysis • landmark_detection.ipynb –
 Landmark detection training and extracting landmarks from video frames
 into csvs. • kinematics_calculation.ipynb – Computes kinematic parameters
 from the landmark data. • kinematics_stats.R – Performs statistical
 analysis on extracted kinematics. • test_1_class.py & test_2_class.py
 – AI classification models. ## Sharing/Access information Data was derived
 from the following sources: • Experimental trials with trained detection
 dogs searching for a target odour on a search wall at the Canine Olfaction
 Research and Education Lab of Texas Tech University. • Videos were
 processed using computer vision techniques to extract tail movement
 features. The raw video recordings used to generate the landmark data are
 available upon request from the corresponding authors. ## Code/Software
 This dataset includes source code for processing and analyzing landmark
 data. The following software and libraries are required: Python • Python
 Version: 3.13.2 • Required Libraries: • numpy, pandas, matplotlib,
 seaborn, scipy • tensorflow, torch, sklearn, tpot, ultralytics • cv2
 (OpenCV), PIL (Pillow), imageio, moviepy, onnx • google, csv, glob, os,
 pickle, random, re • shutil, time, tqdm, zipfile R • R Version: 4.4.2 •
 Required Packages: • tidyverse, dplyr, tidyr, ggplot2, hrbrthemes •
 viridis, viridisLite, visreg, Matrix, lme4 • glmmTMB, TMB, multcomp, car,
 DHARMa