Journal Article Epublish
PubMed: 40809360 DOI: 10.1098/rsos.250399
Does the tail show when the nose knows? Artificial intelligence outperforms human experts at predicting detection dogs finding their target through tail kinematics.
R Soc Open Sci 12(8):250399
Authors: George Martvel, Giulia Pedretti, Teddy Lazebnik, Anna Zamansky, Yuri Ouchi, Tiago Monteiro, Nareed Farhat, Ilan Shimshoni, Yuval Michaeli, Paola Valsecchi, Nathaniel Hall, Sarah Marshall-Pescini, Dan Grinstein
Affiliations
- Department of Information Systems, University of Haifa, Haifa, Israel.
- Department of Medicine and Surgery, University of Parma, Parma, Italy.
- Department of Chemistry, Life Science and Environmental Sustainability, University of Parma, Parma, Italy.
- Department of Mathematics, Ariel University, Ariel, Israel.
- Department of Cancer Biology, University College London, London, UK.
- University of Tsukuba, Tsukuba, Japan. University of Veterinary Medicine Vienna, Vienna, Austria.
- University of Veterinary Medicine Vienna, Vienna, Austria.
- University of Aveiro, Aveiro, Portugal.
- Department of Education and Psychology, University of Aveiro, Aveiro, Portugal.
- Department of Evolutionary and Functional Biology, University of Parma, Parma, Emilia-Romagna, Italy.
- Animal and Food Science, Texas Tech University, Lubbock, TX, USA.
- Domestication Lab, Konrad Lorenz Institute of Ethology, University of Veterinary Medicine, Vienna, Austria.
Abstract
Detection dogs are utilized for searching and alerting to various substances due to their olfactory abilities. Dog trainers report being able to 'predict' such identification based on subtle behavioural 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 odour on a search wall, alerting to its presence by standing still. Dogs' detection accuracy against a distractor odour was 100% with trained concentration, while during threshold assessment, it progressively reached 50%. In the target odour area, dogs exhibited a higher left-sided tail-wagging amplitude. An artificial intelligence (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 odour. 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' behavioural repertoire during odour discrimination.
MeSH
Intelligence, Classification, Artificial Intelligence, Nose, Movement