Multi Species Archives - Tech4Animals

Animal Health Informatics: Towards a generic framework for automatic behavior analysis: Position paper

annazam — Tue, 20 Aug 2019 13:29:49 +0000

This paper introduces an ongoing project that is developing the Blyzer framework, a generic, video-based approach for automatic analysis of animal behavior. The field of veterinary healthcare informatics is still in its early stages, and state-of-the-art solutions from human healthcare are not easily adaptable. The Blyzer framework addresses this by providing a low-cost, simple solution that can be reused across various animal species and analytical tasks, supporting the call for more objective and quantifiable behavior assessment. It comprises two main layers: a computer vision module for tracking animals and a sense-making module for interpreting the extracted data based on health-related tasks. A concrete application of this framework is its use in decision support for behavioral veterinarians, particularly for objectively assessing conditions like ADHD-like behavior in domestic dogs by measuring erratic movement during consultations. The authors emphasize the potential for this framework to facilitate the growth of animal health informatics, promote digitalization in veterinary science and animal welfare, and encourage cross-fertilization between human and animal health informatics.

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A Segment-based Framework for Explainability in Animal Affective Computing

annazam — Mon, 21 Jul 2025 11:49:09 +0000

This paper introduces a novel segment-based framework for enhancing the explainability of deep learning models in animal affective computing, a field that uses technology to understand animal emotions. The core challenge addressed is the “black box” nature of deep learning, which makes it difficult to understand why a model makes a particular decision, hindering trust and adoption among researchers. The proposed framework focuses on evaluating visual explanations, specifically saliency maps, by quantifiably assessing how well they align with biologically meaningful semantic parts of an animal, such as eyes, ears, and mouth. Through case studies on cat pain, horse pain, and dog emotions, the authors demonstrate how this framework can provide quantifiable insights into which facial regions are most important for a classifier’s decision, revealing that the eye area consistently holds the most significance. Ultimately, this framework aims to bridge the gap between complex AI models and expert knowledge, fostering both validation of known indicators and the discovery of new ones in animal behavior.

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Going Deeper than Tracking: A survey of computer-vision based recognition of animal pain and emotions

annazam — Wed, 16 Aug 2023 21:08:41 +0000

This study offers a comprehensive survey of computer vision-based research aimed at recognizing internal affective states in animals, such as pain and emotions, to enhance animal welfare. It specifically focuses on facial and bodily behavior analysis in mammals, extending beyond mere motion tracking. The survey highlights significant challenges, including the lack of a common definition for animal emotions, the inherent difficulty in establishing ground truth due to the absence of verbal communication, data scarcity, and morphological diversity across species. Methodological aspects covered include data collection practices (e.g., recording equipment and environment), annotation strategies (distinguishing between stimulus-based and behavior-based annotations), and data analysis approaches such as using single frames versus spatio-temporal sequences, parts-based versus holistic methods, and hand-crafted versus learned features (deep learning). The authors discuss the “black-box” nature of deep learning models, which can impede explainability crucial for clinical and welfare applications. To advance the field, the paper proposes best practice recommendations for handling data imbalance, applying robust cross-validation techniques (like leave-one-animal-out to ensure subject exclusivity), and exploring domain transfer methodologies. A critical future direction emphasized is the need for public, benchmark datasets to enable standardized comparisons and foster progress in this interdisciplinary area.

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Animal Sentience, Animal Emotions, and Human-Animal Interactions

annazam — Thu, 17 Aug 2023 07:45:55 +0000

This book chapter addresses the challenge of understanding animal emotions given that feelings are subjective and cannot be directly measured. Emotions are defined as multi-component states encompassing subjective, neuro-physiological, cognitive, and behavioral elements that can be objectively measured. The authors emphasize that emotional expressions are species-specific and complex, often leading to human misunderstandings and anthropomorphic biases when interpreting animal cues like a “guilty look” or bared teeth. Accurate assessment requires considering behavioral context and multimodality (e.g., combining facial, body, and vocal cues). The paper also highlights the emerging role of automated emotion recognition using computer vision to overcome human biases, despite challenges such as limited data and the absence of verbal ground truth in animals.

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Who Moved My Culture?: Expanding Anthropological Perspectives in ACI

annazam — Thu, 20 Aug 2020 12:44:09 +0000

This paper explores the potential for cross-fertilization between Animal-Computer Interaction (ACI) and cultural anthropology. It argues for a more systematic integration of anthropological perspectives into ACI, particularly in understanding the animal-technology-human triad within its cultural context. The authors propose that this integration can reveal how culture influences technology-mediated human-animal interactions and, conversely, how these interactions can shape culture. Moving beyond human-centric approaches in technology design for animals, the paper highlights that cultural insights can offer a deeper understanding of animal welfare and human-animal relationships, ultimately enriching both disciplines by re-examining the concept of culture as a multi-species and reciprocal creation.

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