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

Fux, Asaf, Zamansky, Anna, Bleuer-Elsner, Stephane, van der Linden, Dirk, Sinitca, Aleksandr, Romanov, Sergey and Kaplun, Dmitrii (2021) Objective Video-Based Assessment of ADHD-Like Canine Behavior Using Machine Learning. Animals, 11 (10). p. 2806. ISSN 2076-2615

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Official URL: https://doi.org/10.3390/ani11102806


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.

Item Type: Article
Additional Information: Funding information: The research was supported by the grant from the Ministry of Science and Technology of Israel and RFBR according to the research project no. 19-57-06007.
Uncontrolled Keywords: animal-computer interaction, behavioral assessment, veterinary science, machine learning, ADHD-like behavior
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 27 Sep 2021 10:02
Last Modified: 12 Oct 2021 11:30
URI: http://nrl.northumbria.ac.uk/id/eprint/47354

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