vetpressАграрная наукаAgrarian science0869-81552686-701XРедакция журнала "Аграрная наука"10.32634/0869-8155-2025-399-10-58-70vetpress-3862Research ArticleВЕТЕРИНАРИЯVETERINARY MEDICINEСовременные методы и средства оценки проблемного поведения собак-компаньонов: аналитический обзорModern methods and tools for assessing the problematic behavior of companion dogs: an analytical reviewhttps://orcid.org/0000-0002-4374-9244ФоминаА. С.FominaA. S.

Анна Сергеевна Фомина - кандидат биологических наук, доцент,

пл. им. Гагарина, 1, Ростов-на-Дону, 344003

Anna Sergeevna Fomina - Candidate of Biological Sciences, Associate Professor,

1 Gagarin Square, Rostov-on-Don, 344003

a_bogun@mail.ruhttps://orcid.org/0000-0003-4112-7449ВасильевП. В.VasilievP. V.

Павел Владимирович Васильев - кандидат технических наук,

пл. им. Гагарина, 1, Ростов-на-Дону, 344003

Pavel Vladimirovich Vasiliev - Candidate of Technical Sciences,

1 Gagarin Square, Rostov-on-Don, 344003

lyftzeigen@mail.ruhttps://orcid.org/0009-0006-5475-5056КрикуноваА. А.KrikunovaA. A.

Анастасия Анатольевна Крикунова - соискатель,

пл. им. Гагарина, 1, Ростов-на-Дону, 344003

Anastasia Anatolyevna Krikunova - applicant,

1 Gagarin Square, Rostov-on-Don, 344003

akrikunova@donstu.ruЕрмаковА. М.ErmakovA. M.

Алексей Михайлович Ермаков - доктор биологических наук, профессор,

пл. им. Гагарина, 1, Ростов-на-Дону, 344003

Alexey Mikhailovich Ermakov - Doctor of Biological Sciences, Professor,

1 Gagarin Square, Rostov-on-Don, 344003

amermakov@ya.ruДонской государственный технический университетРоссияDon State Technical UniversityRussian Federation2025021120250105870Copyright © Фомина А.С., Васильев П.В., Крикунова А.А., Ермаков А.М., 20252025Фомина А.С., Васильев П.В., Крикунова А.А., Ермаков А.М.Fomina A.S., Vasiliev P.V., Krikunova A.A., Ermakov A.M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.vetpress.ru/jour/article/view/3862

Потребность в разработке и применении комплексных методов анализа поведения собак-компаньонов связана с возможностью раннего выявления поведенческих признаков снижения благополучия. Проведение опросов и ретроспективный анализ данных могут показывать недостаточную точность. Визуальное наблюдение человеком в процессе анализа поведения сопровождается высокой трудоемкостью и вероятностью погрешности ввиду субъективности восприятия. С этой позиции более валидную информацию позволяет получить использование видеозаписей, датчиков двигательной активности и применение нейронных сетей для анализа данных. Проведенный анализ источников позволил выявить слабую разработанность в российской литературе возможности применения технологии искусственного интеллекта в анализе поведения животных. В связи с этим в данном обзоре освещается вопрос применения метода акселерометрии и систем компьютерного зрения для анализа уровня двигательной активности у собак. Выявление поведенческих паттернов и их динамика в заданный временной интервал могут быть использованы для оценки психоэмоционального состояния, уровня тревоги и стресса, особенностей взаимодействия с человеком, другими животными и поведения в разных ситуациях. Можно предполагать, что данный комплексный подход создаст возможность объективного выявления собак, имеющих нарушения поведения. В совокупности с анализом вокализаций это может предоставить боле точную и подробную информацию о коммуникации собак, их психоэмоциональном состоянии, а также способствовать раннему выявлению неблагополучия.

The need to develop and apply comprehensive methods for analyzing the behavior of companion dogs is associated with the possibility of early detection of behavioral signs of decreased well-being. Conducting surveys and retrospective data analysis may show insufficient accuracy. Visual observation by a person in the process of analyzing behavior is accompanied by high labor intensity and the probability of error due to the subjectivity of perception. From this perspective, the use of video recordings, sensors of motor activity, and the use of neural networks for data analysis can provide more valid information. The analysis revealed a weak understanding in the Russian literature of the possibility of using artificial intelligence technology in the analysis of animal behavior. In this regard, this review highlights the issue of using accelerometry and computer vision systems to analyze the level of motor activity in dogs. The identification of behavioral patterns and their dynamics in a given time interval can be used to assess the psychoemotional state, the level of anxiety and stress, the characteristics of interaction with humans, other animals and behavior in different situations. It can be assumed that this comprehensive approach will create an opportunity for the objective identification of dogs with behavioral disorders. Together with the analysis of vocalizations, this can provide more accurate and detailed information about the communication of dogs, their psycho-emotional state, and also contribute to the early detection of problems.

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The authors declare that there are no conflicts of interest present.