Dogs Archives - Tech4Animals

Automated Observations of Dogs’ Resting Behaviour Patterns using Artificial Intelligence and their Similarity to Behavioural Observations

annazam — Fri, 16 Aug 2024 20:12:16 +0000

This paper presents the development and evaluation of an automated computer system, BlyzerDS, which utilizes convolutional neural networks (CNNs) to monitor and analyze dogs’ sleep patterns, aiming to mitigate the time-consuming and error-prone nature of traditional direct behavioral observations in animal welfare research. The system was developed and trained using 13,688 videos of mixed-breed adult dogs recorded over 130 nights. For evaluation, the system’s performance was compared to direct human observations using 6000 previously unseen frames, successfully classifying 5430 of them. Key findings revealed that the automated system achieved an 89% similarity score to manual observations in detecting and quantifying sleep duration and fragmentation. Although no significant difference was found in the percentage of time dogs spent asleep compared to human observers, the automated system recorded more total sleep time, highlighting its capacity to capture more extensive data and address potential limitations like human observer fatigue. The study concludes that this CNN-based system is a reliable and valuable tool that can potentially enhance animal behavior and welfare research by optimizing data collection, increasing precision, and improving replicability, with future potential for adapting to other behaviors and species

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Pets without PETs: on pet owners’ under-estimation of privacy concerns in pet wearables

annazam — Thu, 20 Aug 2020 12:52:12 +0000

This paper explores the difference in privacy concerns expressed about pet wearables versus human wearables, and the underlying reasons for this disparity. A mixed-method study, involving analysis of over 8,000 pet wearable and 20,000 human wearable product reviews, along with a survey of 201 pet owners, found that very few privacy concerns are mentioned overall, but they are expressed 2.3 times less frequently for pet wearables. This underestimation is attributed to emotional drivers (e.g., fear of a pet going missing), the prioritization of desired functionality (like real-time tracking or storing medical records), and consumers’ failure to connect pet data collection to their own or others’ privacy. The study also highlights the significant privacy implications arising from the unintended use of pet wearables to track humans, including cognitively impaired individuals and children. The authors suggest that addressing these issues requires a deeper understanding of consumer behavior and ensuring data protection legislation is robust enough for diverse, real-world use cases.

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Environmental Enrichments and Data-driven Welfare Indicators for Sheltered Dogs using Telemetric Physiological Measures and Signal Processing

annazam — Sat, 17 Aug 2024 08:13:13 +0000

This study examined how different environmental enrichments affect the welfare of eight male shelter dogs in Italy, using telemetric physiological measures to develop objective welfare indicators. Researchers collected heart rate, muscle activity, and body temperature data over 28 days, introducing enrichments including objects, human presence, and female dog companionship. They developed new welfare metrics: predictability (similarity to baseline), sleep quality, and day-night cyclicity. Results showed all enrichments improved welfare, with female companionship being most effective, positively affecting sleep quality and physiological cyclicity. Human presence also showed benefits even with limited interaction, while entertaining objects had less impact. The study provides objective measures for shelter management, emphasizing the importance of social enrichment, particularly with conspecifics, for improving sheltered dogs’ welfare.

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Computational Analysis of Movement Patterns of Dogs with ADHD-like Behavior

annazam — Tue, 20 Aug 2019 12:53:04 +0000

This paper presents a study that uses a self-developed computational tool, K9-Blyzer, to objectively analyze movement patterns of dogs with ADHD-like behavior from video footage collected during veterinary consultations. The study compared 12 dogs diagnosed with ADHD-like behavior (H-group) to a control group of 12 dogs with no behavioral problems (C-group). The computational analysis identified seven significant differences in movement parameters between the groups, leading to the identification of three characteristic dimensions for dogs with ADHD-like behavior: high speed of movement, large coverage of room space, and frequent re-orientation in room space. These findings suggest that computational tools can potentially provide objective support for the assessment, diagnosis, and clinical treatment of ADHD-like behavior in dogs, complementing owner reports.

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Digitally-Enhanced Dog Behavioral Testing: Getting Help from the Machine

annazam — Wed, 16 Aug 2023 20:50:35 +0000

This study introduces a novel paradigm for digitally-enhanced dog behavioral testing, aiming to provide a more objective, robust, and resource-efficient alternative to traditional subjective and time-consuming methods like questionnaires or observation. Researchers tested 53 dogs using a “Stranger Test” protocol, collecting data from three expert behavioral scores and owner-completed Canine Behavioral Assessment and Research Questionnaires (C-BARQ). Utilizing an automated computational approach, specifically the BLYZER system, dog trajectories were extracted and clustered in a “human-free” way from video footage. The unsupervised clustering revealed two distinct behavioral profiles, showing a significant difference in stranger-directed fear C-BARQ scores and effectively separating relaxed dogs from those exhibiting excessive behaviors based on expert evaluations. Furthermore, a machine learning classifier achieved an accuracy of 78% in predicting expert scores of dog coping styles, and a regression model predicted C-BARQ categories with varying success, performing best for Owner-Directed Aggression and Excitability. This research highlights the significant value and promise of AI in the field of dog behavioral assessment, offering a path towards more reliable and objective evaluations.

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The Not So Secret Life of Pets: Pet owners’ privacy concerns for pet location data

annazam — Tue, 20 Aug 2019 12:59:31 +0000

This paper investigates pet owners’ privacy concerns regarding location data collected from pet wearables, specifically for cats and dogs, and how these concerns are influenced by the pet-human bond. The study of 180 participants found no direct difference in general privacy concerns between cat and dog owners. However, there was a significant difference in bonding between them, with dog owners reporting greater closeness and co-sleeping behaviors. Crucially, the research revealed that dog owners exhibit greater concern for how their pet’s location data is used, and this concern significantly correlates with the strength of their bond, particularly in terms of closeness and co-sleeping behaviors. This correlation was absent in cat owners. The authors suggest that for dog owners, these concerns may stem from safety fears like pet theft (especially for co-sleeping pets in predictable locations) or from viewing their pet as an “extended self,” meaning the pet’s location data is perceived as sensitive personal data about themselves. These findings emphasize that the relationship to the pet, rather than the species itself, impacts privacy concerns and should inform the design of privacy-respectful pet wearables.

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BrachySound: Machine learning based assessment of respiratory sounds in dogs

annazam — Wed, 16 Aug 2023 20:52:14 +0000

This study investigated the use of machine learning (ML) models for the objective analysis and diagnosis of Brachycephalic Obstructive Airway Syndrome (BOAS) in dogs, aiming to overcome the subjectivity and time-intensive nature of traditional diagnostic methods like pharyngolaryngeal auscultation. Researchers analyzed 366 audio samples collected from 69 Pugs and 79 other brachycephalic breeds using an electronic stethoscope during a 15-minute standardized exercise test and at rest. The ML models, which included K-Nearest Neighbors (KNN) and Decision Tree classifiers, were developed to classify BOAS test results, predict outcomes from recordings at rest, and detect laryngeal sounds. The results demonstrated significant potential, with models achieving a peak accuracy of 0.85 (85%) for classifying BOAS test results when using combined post-exercise data from Pugs. Predictions based on recordings at rest showed accuracies of 0.68 (68%) for Pugs and 0.65 (65%) for various brachycephalic breeds. Notably, the detection of laryngeal sounds reached an F1 score of 0.80 (80%). These findings suggest that machine learning can streamline the examination process and provide a more objective and efficient approach to canine health assessment, facilitating earlier and more standardized BOAS diagnostics.

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Does my Dog Really Need a Gadget?: What can we learn from pet owners’ amotivations for using pet wearables?

annazam — Tue, 20 Aug 2019 13:01:24 +0000

This paper investigates the barriers to the adoption of pet wearables by pet owners, specifically focusing on “amotivations” – reasons for not purchasing or using these devices. Through a study involving 200 pet owners, the research found that perceived lack of durability and high entry cost are the main barriers to adoption. The qualitative analysis further revealed a complex interplay of various concerns, including cost, durability, welfare, perceived usefulness, accuracy, privacy, technophobia, lack of information, and usability, which often interact and strengthen each other, creating a “snowball effect” that hinders adoption. The study contributes to understanding what constitutes “deal-breakers” for pet owners, suggesting that manufacturers need to address these interconnected concerns.

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Explainable Automated Recognition of Emotional States from Canine Facial Expressions: The case of positive anticipation and frustration

annazam — Wed, 17 Aug 2022 06:59:08 +0000

This study explores the automated recognition of emotional states, specifically positive anticipation and frustration, from canine facial expressions. Utilizing a dataset of Labrador Retrievers in a controlled experimental setting, the researchers compared two distinct approaches: a DogFACS-based method and a deep learning technique. The DogFACS-based approach, which used either 11 or 39 DogFACS variables, achieved an accuracy of over 71% when using the full set of 39 variables. The deep learning approach, particularly with a DINO-ViT backbone, outperformed the DogFACS-based method with over 89% accuracy. The research also significantly contributes to the explainability of AI models in animal emotion recognition, providing decision trees for the DogFACS approach and visual heatmaps for the deep learning approach to show how decisions are made, potentially revealing subtle cues not visible to the human eye.

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Some Might Freak Out: What if your dog’s activity tracker were to have a data breach?

annazam — Tue, 20 Aug 2019 13:05:45 +0000

This paper reports on a mixed-method study (N=61) that investigated dog owners’ privacy concerns regarding data captured by their dog’s activity tracker and the potential consequences of a hypothetical data breach. The study found that many participants perceived few or no consequences from a data leak, often attributing potential issues to “others”. When concerns did arise, they predominantly focused on physical safety threats such as dog theft or burglary, rather than privacy. These perceived consequences were moderately correlated with the trustworthiness of the device manufacturer and the perceived benefit of the device for society as a whole. The authors highlight a worrying information asymmetry where dog owners largely underestimate the value and sensitive nature of this data, which can reveal details about their own routines, health, and caregiving practices, while service providers actively integrate and leverage it within broader data ecosystems (e.g., with veterinarians, insurance companies, and pet service providers). The paper concludes that it is crucial to educate consumers about the full privacy implications of pet activity data to enable them to make informed decisions.

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