Computational analysis of movement patterns of dogs with ADHD-like behavior

Stephane Bleuer-Elsner, Anna Zamansky*, Asaf Fux, Dmitry Kaplun, Sergey Romanov, Aleksandr Sinitca, Sylvia Masson, Dirk van der Linden

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
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Abstract

Computational approaches were called for to address the challenges of more objective behavior assessment which would be less reliant on owner reports. This study aims to use computational analysis for investigating a hypothesis that dogs with ADHD‐like (attention deficit hyperactivity disorder) behavior exhibit characteristic movement patterns directly observable during veterinary consultation. Behavioral consultations of 12 dogs medically treated due to ADHD‐like behavior were recorded, as well as of a control group of 12 dogs with no reported behavioral problems. Computational analysis with a self‐developed tool based on computer vision and machine learning was performed, analyzing 12 movement parameters that can be extracted from automatic dog tracking data. Significant differences in seven movement parameters were found, which led to the identification of three dimensions of movement patterns which may be instrumental for more objective assessment of ADHD‐like behavior by clinicians, while being directly observable during consultation. These include (i) high speed, (ii) large coverage of space, and (iii) constant re‐orientation in space. Computational tools used on video data collected during consultation have the potential to support quantifiable assessment of ADHD‐like behavior informed by the identified dimensions.

Original languageEnglish
Article number1140
JournalAnimals
Volume9
Issue number12
DOIs
Publication statusPublished - 13 Dec 2019
Externally publishedYes

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