Analysis of Dogs’ Sleep Patterns Using Convolutional Neural Networks

Zamansky, Anna; Sinitca, Aleksandr M.; Kaplun, Dmitry; Plazner, Michael; Schork, Ivana Gabriela; Young, Robert J.; Azevedo, Cristiano Schetini de

doi:10.1007/978-3-030-30508-6_38

Cited by 21 publications

(18 citation statements)

References 27 publications

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“…The hypothesis of a preference towards their owner was tested. The Blyzer system [37] was applied to measure a possible preference . The crucial question was how to measure such preference, and which parameters to choose as representation of the dog's interest in a particular screen.…”

Section: Motivational Examplementioning

confidence: 99%

“…The Blyzer tool [6,17,37] is an exception in the sense that it takes as input standard video footage that can be recorded using even low quality web or security cameras. Blyzer aims to provide automatic analysis of animal behavior with minimal restrictions on the animal's environment (unlike tracking systems designed for rodents, e.g.…”

Section: Automatic Analysis Of Animal Behaviormentioning

confidence: 99%

“…Blyzer has already been used for a variety of scientific projects. One example is the analysis of time budget and sleeping patterns of breeding stock kenneled dogs as welfare indicators [37]. The dogs, bred and maintained by the Animal Science Center in Brazil, were observed for eight consecutive months using simple security cameras installed in their kennels (using night vision during the night).…”

Section: Automatic Analysis Of Animal Behaviormentioning

confidence: 99%

“…Another generic framework is DeepLabCut [20], which uses deep neural networks for markerless animal pose estimation. Blyzer [37] similarly uses deep learning for analysis of animal movement patterns. The increasing abilities of CABA approaches and tools mean intelligent systems are getting better at detecting the animal and its movement patterns, generating larger (and more accurate) volumes of movement data.…”

Section: Introductionmentioning

confidence: 99%

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Towards a Methodology for Data-Driven Automatic Analysis of Animal Behavioral Patterns

Menaker

Zamansky

Linden

et al. 2020

Proceedings of the Seventh International Conference on Animal-Computer Interaction

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Measurement of behavior a major challenge in many animalrelated disciplines, including ACI. This usually requires choosing specific parameters for measuring, related to the investigated hypothesis. Therefore, a key challenge is determining a priori what parameters are informational for a given experiment. The scope of this challenge is raised even further by the emerging computational approaches for animal detection and tracking, as automatizing behavioral measurement makes the possibilities for measuring behavioral parameters practically endless. This paper approaches these challenges by proposing a framework for guiding the decision making of researchers in their future data analysis. The framework is data-driven in the sense that it applies data mining techniques for obtaining insights from experimental data for guiding the choice of certain behavioral parameters. Here, we demonstrate the approach using a concrete example of clustering-based analysis of trajectories which can identify 'prevalent areas of stay' of the animal subjects in the experimental setting. CCS CONCEPTS• Human-centered computing → HCI theory, concepts and models; • Information systems → Data mining.

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Section: Motivational Examplementioning

confidence: 99%

Section: Automatic Analysis Of Animal Behaviormentioning

confidence: 99%

Section: Automatic Analysis Of Animal Behaviormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards a Methodology for Data-Driven Automatic Analysis of Animal Behavioral Patterns

Menaker

Zamansky

Linden

et al. 2020

Proceedings of the Seventh International Conference on Animal-Computer Interaction

Self Cite

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“…Deep learning has shown advanced advantages in the field of animal computing, such as animal behavior analysis [18], animal recognition, and species classification [19,20], etc. In aquaculture, convolutional neural networks (CNNs) have gradually become the mainstream research model.…”

Section: Introductionmentioning

confidence: 99%

Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model

Zhang

Yang

Wang

et al. 2020

Animals

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In intensive aquaculture, the number of fish in a shoal can provide valuable input for the development of intelligent production management systems. However, the traditional artificial sampling method is not only time consuming and laborious, but also may put pressure on the fish. To solve the above problems, this paper proposes an automatic fish counting method based on a hybrid neural network model to realize the real-time, accurate, objective, and lossless counting of fish population in far offshore salmon mariculture. A multi-column convolution neural network (MCNN) is used as the front end to capture the feature information of different receptive fields. Convolution kernels of different sizes are used to adapt to the changes in angle, shape, and size caused by the motion of fish. Simultaneously, a wider and deeper dilated convolution neural network (DCNN) is used as the back end to reduce the loss of spatial structure information during network transmission. Finally, a hybrid neural network model is constructed. The experimental results show that the counting accuracy of the proposed hybrid neural network model is up to 95.06%, and the Pearson correlation coefficient between the estimation and the ground truth is 0.99. Compared with CNN- and MCNN-based methods, the accuracy and other evaluation indices are also improved. Therefore, the proposed method can provide an essential reference for feeding and other breeding operations.

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The Future of Technology and Computers in Veterinary Medicine

Byrne¹,

Logas²

2021

Diagnostics and Therapy in Veterinary Dermatology

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Analysis of Dogs’ Sleep Patterns Using Convolutional Neural Networks

Abstract: Ti t l e An alysis of d o g s' sl e e p p a t t e r n s u si n g c o nvol u tio n al n e u r al n e t w o r k s

Cited by 21 publications

References 27 publications

Towards a Methodology for Data-Driven Automatic Analysis of Animal Behavioral Patterns

Towards a Methodology for Data-Driven Automatic Analysis of Animal Behavioral Patterns

Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model

The Future of Technology and Computers in Veterinary Medicine

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