Direct and correlated effects of various selection strategies in performance tests of bulls

International audienc

Introduction: Sensations, Symptoms and Healthcare Seeking

This is a post-peer-review, pre-copyedited version of an article published in Anthropology in Action. The definitive publisher-authenticated version Andersen, R.S., Nichter, M. & Risør, M.B. (2017). Introduction. Sensations, Symptoms and Healthcare Seeking. Anthropology in Action. 24(1), 1-5 is available online at: https://doi.org/10.3167/aia.2017.240101.Inspired by the sensory turn in the humanities, anthropologists have coined the term ‘an anthropology of the senses’ to describe the study of the perceptual construction and output of bodily sensations and sense-modalities (cf. Howes 2006; Nichter 2008). Starting from the premise that different cultures and social settings configure, elaborate and extend the senses in different directions, key proponents have argued for a greater empirical and analytical attention to the cultural embeddedness and socio-biological basis of bodily perception and experience. This follows a rethinking of a series of theoretical (cf. Hinton et al. 2008; Ingold 2011) and methodological commitments in anthropology (cf. Pink 2009; Stoller 2004) that also holds relevance for anthropological studies of health and illness, which is the focus of this special issue on sensations, symptoms and healthcare seeking

Equine Facial Action Coding System for determination of pain-related facial responses in videos of horses

During the last decade, a number of pain assessment tools based on facial expressions have been developed for horses. While all tools focus on moveable facial muscles related to the ears, eyes, nostrils, lips, and chin, results are difficult to compare due to differences in the research conditions, descriptions and methodologies. We used a Facial Action Coding System (FACS) modified for horses (EquiFACS) to code and analyse video recordings of acute short-term experimental pain (n = 6) and clinical cases expected to be in pain or without pain (n = 21). Statistical methods for analyses were a frequency based method adapted from human FACS approaches, and a novel method based on co-occurrence of facial actions in time slots of varying lengths. We describe for the first time changes in facial expressions using EquiFACS in video of horses with pain. The ear rotator (EAD104), nostril dilation (AD38) and lower face behaviours, particularly chin raiser (AU17), were found to be important pain indicators. The inner brow raiser (AU101) and eye white increase (AD1) had less consistent results across experimental and clinical data. Frequency statistics identified AUs, EADs and ADs that corresponded well to anatomical regions and facial expressions identified by previous horse pain research. The co-occurrence based method additionally identified lower face behaviors that were pain specific, but not frequent, and showed better generalization between experimental and clinical data. In particular, chewing (AD81) was found to be indicative of pain. Lastly, we identified increased frequency of half blink (AU47) as a new indicator of pain in the horses of this study

Dynamics are Important for the Recognition of Equine Pain in Video

A prerequisite to successfully alleviate pain in animals is to recognize it, which is a great challenge in non-verbal species. Furthermore, prey animals such as horses tend to hide their pain. In this study, we propose a deep recurrent two-stream architecture for the task of distinguishing pain from non-pain in videos of horses. Different models are evaluated on a unique dataset showing horses under controlled trials with moderate pain induction, which has been presented in earlier work. Sequential models are experimentally compared to single-frame models, showing the importance of the temporal dimension of the data, and are benchmarked against a veterinary expert classification of the data. We additionally perform baseline comparisons with generalized versions of state-of-the-art human pain recognition methods. While equine pain detection in machine learning is a novel field, our results surpass veterinary expert performance and outperform pain detection results reported for other larger non-human species.Comment: CVPR 2019: IEEE Conference on Computer Vision and Pattern Recognitio