Objective Video-Based Assessment of ADHD-Like Canine Behavior Using Machine Learning

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Canine ADHD-like behavior is a behavioral problem that often compromises dogs’ well-being, as well as the quality of life of their owners; early diagnosis and clinical intervention are often critical for successful treatment, which usually involves medication and/or behavioral modification. Diagnosis mainly relies on owner reports and some assessment scales, which are subject to subjectivity. This study is the first to propose an objective method for automated assessment of ADHD-like behavior based on video taken in a consultation room. We trained a machine learning classifier to differentiate between dogs clinically treated in the context of ADHD-like behavior and health control group with 81% accuracy; we then used its output to score the degree of exhibited ADHD-like behavior. In a preliminary evaluation in clinical context, in 8 out of 11 patients receiving medical treatment to treat excessive ADHD-like behavior, H-score was reduced. We further discuss the potential applications of the provided artifacts in clinical settings, based on feedback on H-score received from a focus group of four behavior experts.
Original languageEnglish
Article number2806
Number of pages27
JournalAnimals
Volume11
Issue number10
DOIs
Publication statusPublished - 26 Sep 2021

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