Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

Assessing machine learning classifiers for the detection of animals' behavior using depth-based tracking

[EN] There is growing interest in the automatic detection of animals' behaviors and body postures within the field of Animal Computer Interaction, and the benefits this could bring to animal welfare, enabling remote communication, welfare assessment, detection of behavioral patterns, interactive and adaptive systems, etc. Most of the works on animals' behavior recognition rely on wearable sensors to gather information about the animals' postures and movements, which are then processed using machine learning techniques. However, non-wearable mechanisms such as depth-based tracking could also make use of machine learning techniques and classifiers for the automatic detection of animals' behavior. These systems also offer the advantage of working in set-ups in which wearable devices would be difficult to use. This paper presents a depth-based tracking system for the automatic detection of animals' postures and body parts, as well as an exhaustive evaluation on the performance of several classification algorithms based on both a supervised and a knowledge-based approach. The evaluation of the depth -based tracking system and the different classifiers shows that the system proposed is promising for advancing the research on animals' behavior recognition within and outside the field of Animal Computer Interaction. (C) 2017 Elsevier Ltd. All rights reserved.This work is funded by the European Development Regional Fund (EDRF-FEDER) and supported by Spanish MINECO with Project TIN2014-60077-R. It also received support from a postdoctoral fellowship within the VALi+d Program of the Conselleria d'Educacio, Cultura I Esport (Generalitat Valenciana) awarded to Alejandro Catala (APOSTD/2013/013). The work of Patricia Pons is supported by a national grant from the Spanish MECD (FPU13/03831). Special thanks to our cat participants and their owners, and many thanks to our feline caretakers and therapists, Olga, Asier and Julia, for their valuable collaboration and their dedication to animal wellbeing.Pons Tomás, P.; Jaén Martínez, FJ.; Catalá Bolós, A. (2017). Assessing machine learning classifiers for the detection of animals' behavior using depth-based tracking. Expert Systems with Applications. 86:235-246. https://doi.org/10.1016/j.eswa.2017.05.063S2352468

DoggyVision: Examining how Dogs (Canis familiaris) Interact with Media using a Dog-Driven Proximity Tracker Device

With screen technology becoming ubiquitously embedded into our homes, these screens are often in places where they can be viewed by domestic dogs (Canis familiaris); however, there is a lack of research showing to what extent, and for how long, dogs attend to media on screens. One pressing question is to understand if a dog, given the opportunity, would or could control its own viewing. This paper describes a prototype system (DoggyVision) that gives control to a dog in regard to the turning on and off of a TV screen in order to study activation with screen media in home settings. The system is used with two dogs to explore the interaction modalities between machine and dog. DoggyVision is shown to be non-invasive for the dog and easy to use in the home. Recordings show that dogs did attend to the screen but did not appear, in this study, to change their activation behaviors around the TV screen between being in no control (week 1), and in some control (week 2), of the TV media presentation. The study builds on ‘dog-centered’ methods to examine a dog’s behavior non-invasively demonstrating that useful data can be yielded from dog-driven devices within the home. For the Animal Computer Interaction community, this is the first system that allows the dog to trigger the activation of the device as the system records the activation automatically

The use of wearable sensors for animal behaviour assessment

PhD ThesisThe research outlined in this thesis presents novel applications of wearable sensors in the domain of animal behaviour assessment. The use of wearable sensing technology, and in particular accelerometry, has become a mainstay of behaviour assessment in humans, allowing for detailed analysis of movement based behaviour and health monitoring. In this thesis we look to apply these methodologies to animals and identify approaches towards monitoring their health and wellbeing. We investigate the use of the technology in the animal domain through a series of studies examining the problem across multiple species and in increasingly complex scenarios. A tightly constrained scenario is presented initially, in which horse behaviour was classi ed and assessed in the context of dressage performances. The assessment of lying behaviour in periparturient sows con ned to gestation crates examines a scenario in which the movement of the subject was constrained, but not predetermined. Expanding this work to include sows housed in free-farrowing environments removed the movement constraints imposed by the gestation crates. We examine the implications of the use of multiple sensors and how this might a ect the accuracy of the assessments. Finally, a system for behaviour recognition and assessment was developed for domestic cats. Study animals were free to move and behave at their own discretion whilst being monitored through the use of wearable sensors, in the least constrained of the studies. The scenarios outlined herein describe applications with an increasing level of complexity through the removal of constraints. Through this work we demonstrate that these techniques are applicable across species and hold value for the wellbeing of both commercial and companion animals.European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement number 613574 (PROHEALTH). This project has also received funding from the Biotechnology and Biological Sciences Research Council (BBSRC) in the form of a studentshi

Towards a Canine-Human Communication System Based on Head Gestures

© Copyright 2015 ACMPresented at the 2nd International Congress on Animal-Computer Interaction at ACE’15, November 16-19, 2015, Iskandar, Malaysia.DOI: http://dx.doi.org/10.1145/2832932.2837016We explored symbolic canine-human communication for working dogs through the use of canine head gestures. We identified a set of seven criteria for selecting head gestures and identified the first four deserving further experimentation. We devised computationally inexpensive mechanisms to prototype the live system from a motion sensor on the dog’s collar. Each detected gesture is paired with a predetermined message that is voiced to the humans by a smart phone. We examined the system and proposed gestures in two experiments, one indoors and one outdoors. Experiment A examined both gesture detection accuracy and a dog’s ability to perform the gestures using a predetermined routine of cues. Experiment B examined the accuracy of this system on two outdoor working-dog scenarios. The detection mechanism we presented is sufficient to point to improvements into system design and provide valuable insights into which gestures fulfill the seven minimum criteria