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

Fux, Asaf; Zamansky, Anna; Bleuer-Elsner, Stephane; Linden, Dirk van der; Sinitca, Aleksandr M.; Romanov, S. A.; Kaplun, Dmitry

doi:10.3390/ani11102806

Cited by 18 publications

(25 citation statements)

References 62 publications

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“…Only a few canine behaviour studies to date applied machine-learning mediated tracking, most of them using tracking based on a single camera (e.g., Barnard et al, 2016;Bleuer-Elsner et al, 2019;Fux et al, 2021;Karl et al, 2020;Zamansky et al, 2018). These studies provided evidence that dogs with a deprivation syndrome moved less compared to normal controls when presented with a robot dog (Zamansky et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Using machine learning to track dogs’ exploratory behavior in presence and absence of their caregiver

Völter¹,

Starić²,

Huber³

2022

Preprint

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The dog-human relationship exhibits characteristics of human attachment bonds. In particular, dogs have been found to show a secure base effect, i.e., they explored and played more in the presence of their owner as compared to a stranger. In the current study, we presented the dogs with a procedure in which the dogs could move freely in a room equipped with trial-unique objects. We manipulated across trials whether the owner and/or a stranger was present in the room or not. Crucially, we applied and validated machine learning to track the dogs’ behavior throughout the trials. The three-dimensional (3D) tracking of the dogs’ bodies allowed us to extract behavioral responses with high spatial and temporal resolution without relying on subjective human ratings. The results show that both the owner and stranger significantly increased the dogs’ exploration; the dogs also spend more time in the proximity of the owner and stranger location when they were present. Even though the presence of owner and stranger had a significant effect on the dogs’ behavior, the effect of the owner was more pronounced. We conclude that machine-driven 3D tracking provides a reliable access for detailed behavioral analyses of dogs’ exploration and attachment-related behaviors.

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Section: Discussionmentioning

confidence: 99%

Using machine learning to track dogs’ exploratory behavior in presence and absence of their caregiver

Völter¹,

Starić²,

Huber³

2022

Preprint

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“…Intensity of use is an animal movement metric used in ( 47 ). We decided to include this feature due to its usefulness in ( 30 ) in the context of dog behavior analysis.…”

Section: The Clustering Methodsmentioning

confidence: 99%

“…We decided to include this feature due to its usefulness in (30) in the context of dog behavior analysis. 3.…”

Section: Remarksmentioning

confidence: 99%

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Clustering for Automated Exploratory Pattern Discovery in Animal Behavioral Data

et al. 2022

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Traditional methods of data analysis in animal behavior research are usually based on measuring behavior by manually coding a set of chosen behavioral parameters, which is naturally prone to human bias and error, and is also a tedious labor-intensive task. Machine learning techniques are increasingly applied to support researchers in this field, mostly in a supervised manner: for tracking animals, detecting land marks or recognizing actions. Unsupervised methods are increasingly used, but are under-explored in the context of behavior studies and applied contexts such as behavioral testing of dogs. This study explores the potential of unsupervised approaches such as clustering for the automated discovery of patterns in data which have potential behavioral meaning. We aim to demonstrate that such patterns can be useful at exploratory stages of data analysis before forming specific hypotheses. To this end, we propose a concrete method for grouping video trials of behavioral testing of animal individuals into clusters using a set of potentially relevant features. Using an example of protocol for testing in a “Stranger Test”, we compare the discovered clusters against the C-BARQ owner-based questionnaire, which is commonly used for dog behavioral trait assessment, showing that our method separated well between dogs with higher C-BARQ scores for stranger fear, and those with lower scores. This demonstrates potential use of such clustering approach for exploration prior to hypothesis forming and testing in behavioral research.

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“…After reviewing previous studies on dog behavior recognition, we identified related studies [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ], which are summarized in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

Behavior-Based Video Summarization System for Dog Health and Welfare Monitoring

Atif

Lee

Park

et al. 2023

Sensors

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The popularity of dogs has been increasing owing to factors such as the physical and mental health benefits associated with raising them. While owners care about their dogs’ health and welfare, it is difficult for them to assess these, and frequent veterinary checkups represent a growing financial burden. In this study, we propose a behavior-based video summarization and visualization system for monitoring a dog’s behavioral patterns to help assess its health and welfare. The system proceeds in four modules: (1) a video data collection and preprocessing module; (2) an object detection-based module for retrieving image sequences where the dog is alone and cropping them to reduce background noise; (3) a dog behavior recognition module using two-stream EfficientNetV2 to extract appearance and motion features from the cropped images and their respective optical flow, followed by a long short-term memory (LSTM) model to recognize the dog’s behaviors; and (4) a summarization and visualization module to provide effective visual summaries of the dog’s location and behavior information to help assess and understand its health and welfare. The experimental results show that the system achieved an average F1 score of 0.955 for behavior recognition, with an execution time allowing real-time processing, while the summarization and visualization results demonstrate how the system can help owners assess and understand their dog’s health and welfare.

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

Cited by 18 publications

References 62 publications

Using machine learning to track dogs’ exploratory behavior in presence and absence of their caregiver

Using machine learning to track dogs’ exploratory behavior in presence and absence of their caregiver

Clustering for Automated Exploratory Pattern Discovery in Animal Behavioral Data

Behavior-Based Video Summarization System for Dog Health and Welfare Monitoring

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