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

Sharing Pain: Using Pain Domain Transfer for Video Recognition of Low Grade Orthopedic Pain in Horses

Orthopedic disorders are common among horses, often leading to euthanasia, which often could have been avoided with earlier detection. These conditions often create varying degrees of subtle long-term pain. It is challenging to train a visual pain recognition method with video data depicting such pain, since the resulting pain behavior also is subtle, sparsely appearing, and varying, making it challenging for even an expert human labeller to provide accurate ground-truth for the data. We show that a model trained solely on a dataset of horses with acute experimental pain (where labeling is less ambiguous) can aid recognition of the more subtle displays of orthopedic pain. Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on clean experimental pain in the orthopedic dataset. Finally, this is accompanied with a discussion around the challenges posed by real-world animal behavior datasets and how best practices can be established for similar fine-grained action recognition tasks. Our code is available at https://github.com/sofiabroome/painface-recognition

Changes in the equine facial repertoire during different orthopedic pain intensities

A number of facial expressions are associated with pain in horses, however, the entire display of facial activities during orthopedic pain have yet to be described. The aim of the present study was to exhaustively map changes in facial activities in eight resting horses during a progression from sound to mild and moderate degree of orthopedic pain, induced by lipopolysaccharides (LPS) administered in the tarsocrural joint. Lameness progression and regression was measured by objective gait analysis during movement, and facial activities were described by EquiFACS in video sequences (n = 348, total length 892.5 min) of the horses obtained when resting in their box stalls. Predictive modeling identified 16 action units and action descriptors, related to ears, eyes, and lower face. Lower lip depressor (AU16), lips part (AU25), half blink (AU47), single ear forward (SEAD101) and single ear rotator (SEAD104) were selected as co-occurring significantly more in horses with pain than in horses without pain. The major change in co-occurring facial activities occurred in the transition from no pain to mild pain. In conclusion, resting horses with induced orthopedic pain showed a dynamic upper and lower facial repertoire and the relationship between level of pain intensity and facial activity appears complex

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

Going Deeper than Tracking: A Survey of Computer-Vision Based Recognition of Animal Pain and Emotions

Advances in animal motion tracking and pose recognition have been a game changer in the study of animal behavior. Recently, an increasing number of works go 'deeper' than tracking, and address automated recognition of animals' internal states such as emotions and pain with the aim of improving animal welfare, making this a timely moment for a systematization of the field. This paper provides a comprehensive survey of computer vision-based research on recognition of pain and emotional states in animals, addressing both facial and bodily behavior analysis. We summarize the efforts that have been presented so far within this topic-classifying them across different dimensions, highlight challenges and research gaps, and provide best practice recommendations for advancing the field, and some future directions for research

The look of lameness - Behaviors and facial expressions associated with orthopedic pain in horses

There are increasing concerns about equine welfare in equestrian sports, where early detection of orthopedic pain remains a major challenge since reliable and valid pain assessment tools are lacking. Movement asymmetry may be present in horses perceived as free from lameness by their owners, as well as in horses with confirmed orthopedic pain. It is therefore important to differentiate movement asymmetry due to pain from that due to other reasons, which may be achievable by improving orthopedic pain assessment. The aim of this thesis was thus to identify body behaviors and changes in facial activity related to orthopedic pain and movement asymmetry in horses. Progression and regression of movement asymmetry after induced orthopedic pain was monitored and measured with gait analysis in eight horses. A number of behaviors including altered posture, head position, location in the box stall, focus and human interaction were found to be associated with orthopedic pain, as were facial expressions. Only one of four equine pain scales tested detected orthopedic pain reliably and accurately. Dynamic and diverse facial displays were identified in resting and moving horses during pain, illustrating that the concept of one prototypical pain face may be a simplification of the full pain-related facial repertoire. Horses trotted by hand showed a great inter-individual variation in facial expressiveness, highlighting the need for further analysis of facial activity during motion before its use for pain detection. The new knowledge on the relationship between pain and movement asymmetry provided in this thesis, can lead to improved pain assessments, pain management and equine welfare

Social communication in domestic horses: the production and perception of facial expressions

Living in complex societies is thought to promote the development of sophisticated social, cognitive, and communicative skills. Investigating the extent of these skills across taxa is critical to understanding the evolution of the advanced abilities found in some species, including humans. Facial expressions are rich sources of social information for humans and some primates; however whether this is true for other animals is largely unknown. Horses are an ideal study species for these questions: they form valuable social relationships and display some advanced socio-cognitive skills, but are phylogenetically distant from primates and so might be expected to communicate quite differently. Here I present a method for quantifying and coding horse facial movements (EquiFACS), which reveals that horses have an extensive capacity for producing facial expressions. I then utilise EquiFACS to demonstrate that horses produce facial actions that mirror the emotional content of auditory stimuli, providing evidence for a perception-action representation of emotional information. Through my experiments on the perception of facial expressions in horses I show that these expressions display meaningful information to conspecifics, which influences their behaviour in functionally relevant ways. I also shed light on the physiological processes involved in the perception of emotional conspecific facial expressions, showing that viewing negatively valenced conspecific emotional expressions raises resting heart rate. This is indicative of emotional contagion, which may be a mechanism through which information is obtained and social interactions are regulated. Collectively, my research demonstrates the ability to produce and use complex facial expressions as a source of social information is not limited to primates, but is present in at least two phylogenetically distant groups. This suggests these skills may either be an evolutionarily conserved trait or have evolved under common selective pressures. As well as their scientific significance, these findings have implications for horse management and welfare

Horses discriminate between facial expressions of conspecifics

In humans, facial expressions are rich sources of social information and have an important role in regulating social interactions. However, the extent to which this is true in non-human animals, and particularly in non-primates, remains largely unknown. Therefore we tested whether domestic horses (Equus caballus) could discriminate between facial expressions of their conspecifics captured in different contexts, and whether viewing these expressions elicited functionally relevant reactions. Horses were more likely to approach photographic stimuli displaying facial expressions associated with positive attention and relaxation, and to avoid stimuli displaying an expression associated with aggression. Moreover, differing patterns of heart rate changes were observed in response to viewing the positive anticipation and agonistic facial expressions. These results indicate that horses spontaneously discriminate between photographs of unknown conspecifics portraying different facial expressions, showing appropriate behavioural and physiological responses. Thus horses, an animal far-removed from the primate lineage, also have the ability to use facial expressions as a means of gaining social information and potentially regulating social interactions

Development and Preliminary Validation of an Equine Brief Pain Inventory for Owner Assessment of Chronic Pain Due to Osteoarthritis in Horses

An owner-completed questionnaire was designed to monitor the level of chronic pain and impact on quality of life in horses with osteoarthritis (OA). A standardized approach to develop and validate subjective-state scales for clinical use was followed. Scale items were generated through literature review, focus group meetings, and expert panel evaluation. The draft tool was tested for reading level and language ambiguity and piloted in 25 owners/caregivers of horses with osteo-arthritis, with factor analysis performed on responses. The resulting revised questionnaire is cur-rently undergoing validation in a larger sample population of 60 OA and 20 sound control horses. In the pilot group, 21 people (84%) found the questionnaire easy to complete and 22 people (88%) found it useful. It could be completed within 5 min by all participants. Readability scores (Flesch Reading Ease Score, Flesch–Kincaid grade level, SMOG index) indicated an English language reading level comparable to that of 6th to 7th grade in the U.S. system (age 11–12 years). Cronbach’s alpha of all items in the tool was 0.957, indicating excellent inter-item correlation. In-terim analysis for 23 OA horses from the sample population showed good test–retest reliability and higher scores compared to 5 control horses. Full validation must be completed for the in-strument to be used in clinical practice