Development of an Automated Pain Facial Expression Detection System for Sheep (Ovis Aries)

Comparison between AI and human expert performance in acute pain assessment in sheep

This study explores the question whether Artificial Intelligence (AI) can outperform human experts in animal pain recognition using sheep as a case study. It uses a dataset of N = 48 sheep undergoing surgery with video recordings taken before (no pain) and after (pain) surgery. Four veterinary experts used two types of pain scoring scales: the sheep facial expression scale (SFPES) and the Unesp-Botucatu composite behavioral scale (USAPS), which is the 'golden standard' in sheep pain assessment. The developed AI pipeline based on CLIP encoder significantly outperformed human facial scoring (AUC difference = 0.115, p < 0.001) when having access to the same visual information (front and lateral face images). It further effectively equaled human USAPS behavioral scoring (AUC difference = 0.027, p = 0.163), but the small improvement was not statistically significant. The fact that the machine can outperform human experts in recognizing pain in sheep when exposed to the same visual information has significant implications for clinical practice, which warrant further scientific discussion.

Automated acute pain prediction in domestic goats using deep learning-based models on video-recordings

Facial expressions are essential in animal communication, and facial expression-based pain scales have been developed for different species. Automated pain recognition offers a valid alternative to manual annotation with growing evidence across species. This study applied machine learning (ML) methods, using a pre-trained VGG-16 base and a Support Vector Machine classifier to automate pain recognition in caprine patients in hospital settings, evaluating different frame extraction rates and validation techniques. The study included goats of different breed, age, sex, and varying medical conditions presented to the University of Florida's Large Animal Hospital. Painful status was determined using the UNESP-Botucatu Goat Acute Pain Scale. The final dataset comprised images from 40 goats (20 painful, 20 non-painful), with 2,253 'non-painful' and 3,154 'painful' images at 1 frame per second (FPS) extraction rate and 7,630 'non-painful' and 9,071 'painful' images at 3 FPS. Images were used to train deep learning-based models with different approaches. The model input was raw images, and pain presence was the target attribute (model output). For the single train-test split and 5-fold cross-validation, the models achieved approximately 80% accuracy, while the subject-wise 10-fold cross-validation showed mean accuracies above 60%. These findings suggest ML's potential in goat pain assessment.

Critical key points for anesthesia in experimental research involving sheep (Ovis aries)

Anesthesia and analgesia have a major impact on ensuring animal welfare and safety, resulting in reduced stress response and effective pain control, ensuring the comfort of the animal, promoting faster recovery, and reducing the risk of complications associated with various research procedures. Each stage of anesthesia in sheep is vital for maintaining the animal's welfare, ensuring procedural success, minimizing stress, risks, and complications, and optimizing the quality of research data. Proper attention to detail and adherence to best practices at each stage contribute to the overall success of anesthesia management in sheep. Anesthesia protocols should suit individual requirements for each sheep, in light of factors such as health status, procedure duration, and desired anesthesia depth. Meticulous monitoring, adherence to best practices, and prompt intervention are essential for minimizing the risks of complications during sheep anesthesia and ensuring the safety and welfare of the animals undergoing anesthesia for research purposes. This article presents the main key points that can improve anesthetic management for sheep involved in experimental research to minimize stress response and complications, enhancing animal safety, welfare, and comfort during and after procedures. Multimodal anesthesia approaches ensure effective pain relief, tailored to the specific needs of individual animals or procedures, optimizing outcomes, and minimizing risks. Anesthesia management contributes to improved research data collection under conditions that enhance the validity and reliability of results. Sheep's impressive capacity to maintain homeostasis even during extended periods of anesthesia highlights the critical importance of upholding data quality in alignment with the universally accepted principles of replacement, reduction, and refinement for ethical animal research. By adhering to these principles, researchers can minimize the number of animals used, reduce any potential discomfort or distress experienced by the animals, and refine procedures to optimize animal welfare while still achieving scientific objectives.

How to see stress in chickens: On the way to a Stressed Chicken Scale

For many species, scales are used to classify discomfort and stress (e.g., facial expression/pain scales). Although a significant number of vertebrates used for scientific purposes are chickens, a corresponding scale for birds has not yet been established. We developed a Stressed Chicken Scale (SCS) to investigate whether it is possible to assess discomfort in a chicken by its body posture. A selective review with additional handsearch was conducted to find suitable parameters for visual stress assessment. Seven potential body signals were identified: Tail and head position, eye closure, beak opening, leg and wing position, and plumage fullness (ruffled or fluffed up feathers). The SCS was evaluated for interobserver reliability with veterinary students (n = 20), using randomized pictures of stressed and unstressed chickens in lateral view (n = 80). Observers were able to identify the body signals on the pictures after a brief training session. Agreement scores for interobserver agreement ranged from κ = 0.31 (fair agreement) for eye closure to κ = 0.78 (substantial agreement) for beak opening. We found that the number of body signals displayed in a stressed expression had an impact on observers' overall assessment of the chickens, for example, chickens were more likely to be rated as stressed if more than 4 signals indicative of stress were present. We conclude that the 7 individual body signals can be used to identify discomfort in chickens.

From facial expressions to algorithms: a narrative review of animal pain recognition technologies

Facial expressions are essential for communication and emotional expression across species. Despite the improvements brought by tools like the Horse Grimace Scale (HGS) in pain recognition in horses, their reliance on human identification of characteristic traits presents drawbacks such as subjectivity, training requirements, costs, and potential bias. Despite these challenges, the development of facial expression pain scales for animals has been making strides. To address these limitations, Automated Pain Recognition (APR) powered by Artificial Intelligence (AI) offers a promising advancement. Notably, computer vision and machine learning have revolutionized our approach to identifying and addressing pain in non-verbal patients, including animals, with profound implications for both veterinary medicine and animal welfare. By leveraging the capabilities of AI algorithms, we can construct sophisticated models capable of analyzing diverse data inputs, encompassing not only facial expressions but also body language, vocalizations, and physiological signals, to provide precise and objective evaluations of an animal's pain levels. While the advancement of APR holds great promise for improving animal welfare by enabling better pain management, it also brings forth the need to overcome data limitations, ensure ethical practices, and develop robust ground truth measures. This narrative review aimed to provide a comprehensive overview, tracing the journey from the initial application of facial expression recognition for the development of pain scales in animals to the recent application, evolution, and limitations of APR, thereby contributing to understanding this rapidly evolving field.

The grimace scale: a useful tool for assessing pain in laboratory animals

Accurately and promptly assessing pain in experimental animals is extremely important to avoid unnecessary suffering of the animals and to enhance the reproducibility of experiments. This is a key concern for veterinarians, animal caretakers, and researchers from the perspectives of veterinary care and animal welfare. Various methods including ethology, immunohistochemistry, electrophysiology, and molecular biology are used for pain assessment. However, the grimace scale, which was developed by taking cues from interpreting pain through facial expressions of non-verbal infants, has become recognized as a very simple and practical method for objectively evaluating pain levels by scoring changes in an animal's expressions. This method, which was first implemented with mice approximately 10 years ago, is now being applied to various experimental animals and is widely used in research settings. This review focuses on the usability of the grimace scale from the "cage-side" perspective, aiming to make it a more user-friendly tool for those involved in animal experiments. Differences in facial expressions in response to pain in various animals, examples of applying the grimace scale, current automated analytical methods, and future prospects are discussed.

Painful Experiences in Social Contexts Facilitate Sensitivity to Emotional Signals of Pain from Conspecifics in Laboratory Rats

Previous studies demonstrated that laboratory rats could visually receive emotional pain signals from conspecifics through pictorial stimuli. The present study examined whether a prior painful emotional experience of the receiver influenced the sensitivity of emotional expression recognition in laboratory rats. The experiment comprised four phases: the baseline preference test, pain manipulation test, post-manipulation preference test, and state anxiety test. In the baseline phase, the rats explored an apparatus comprising two boxes to which pictures of pain or neutral expressions of other conspecifics were attached. In the pain manipulation phase, each rat was allocated to one of three conditions: foot shock alone (pained-alone; PA), foot shock with other unfamiliar conspecifics (pained-with-other; PWO), or no foot shock (control). In the post-manipulation phase, the animals explored the apparatus in the same manner as they did in the baseline phase. Finally, an open-field test was used to measure state anxiety. These findings indicate that rats in the PWO group stayed longer per entry in a box with photographs depicting a neutral disposition than in a box with photographs depicting pain after manipulation. The results of the open-field test showed no significant differences between the groups, suggesting that the increased sensitivity to pain expression in other individuals due to pain experiences in social settings was not due to increased primary state anxiety. Furthermore, the results indicate that rats may use a combination of self-painful experiences and the states of other conspecifics to process the emotional signal of pain from other conspecifics. In addition, changes in the responses of rats to facial expressions in accordance with social experience suggest that the expression function of rats is not only used for emotional expressions but also for communication.

The groundbreaking impact of digitalization and artificial intelligence in sheep farming

The integration of digitalization and Artificial Intelligence (AI) has marked the onset of a new era of efficient sheep farming in multiple aspects ranging from the general well-being of sheep to advanced web-based management applications. The resultant improvement in sheep health and consequently better farming yield has already started to benefit both farmers and veterinarians. The predictive analytical models embedded with machine learning (giving sense to machines) has helped better decision-making and has enabled farmers to derive most out of their farms. This is evident in the ability of farmers to remotely monitor livestock health by wearable devices that keep track of animal vital signs and behaviour. Additionally, veterinarians now employ advanced AI-based diagnostics for efficient parasite detection and control. Overall, digitalization and AI have completely transformed traditional farming practices in livestock animals. However, there is a pressing need to optimize digital sheep farming, allowing sheep farmers to appreciate and adopt these innovative systems. To fill this gap, this review aims to provide available digital and AI-based systems designed to aid precision farming of sheep, offering an up-to-date understanding on the subject. Various contemporary techniques, such as sky shepherding, virtual fencing, advanced parasite detection, automated counting and behaviour tracking, anomaly detection, precision nutrition, breeding support, and several mobile-based management applications are currently being utilized in sheep farms and appear to be promising. Although artificial intelligence and machine learning may represent key features in the sustainable development of sheep farming, they present numerous challenges in application.

Sheep fatigue during transport: Lost in translation?

Although sheep are commonly transported long distances, and sheep welfare during transport is a topic of research and policy discussion, the subject of their fatigue during transport has been under-researched. The current qualitative study, focused on the EU and UK, aimed to critically analyse stakeholder views on issues relating to sheep fatigue, including behavioural indications of fatigue, the interplay between fatigue and other factors, and the practicalities of identifying fatigue in commercial transport conditions. Insight into stakeholder perceptions of these issues could contribute to the body of knowledge regarding sheep fatigue during transport, potentially playing a part in future efforts to improve fatigue understanding and detection. Eighteen experts from different stakeholder groups were interviewed. Reflexive thematic analysis of interview data yielded four themes and three sub-themes. The first theme, "Let's anthropomorphise it a little bit", underscores the pervasiveness of anthropomorphism and suggests using it in a conscious and deliberate way to drive stakeholder engagement and policy change. The second theme, "We think that they're like we are and they're not", cautions against wholesale transfer of human experiences to animals. The third theme, 'See the whole animal', advocates using Qualitative Behaviour Analysis (QBA), proven reliable in other contexts, to deepen and enrich our current understanding of fatigue. The fourth theme, 'Fatigue "never comes up"', highlights the fact that fatigue is rarely if ever discussed in the context of sheep transport. These themes suggest several avenues for future research, including developing QBA-based assessments for fatigue to improve welfare during transport.

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 osteoarthritis, with factor analysis performed on responses. The resulting revised questionnaire is currently 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. Interim 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 instrument to be used in clinical practice.

Pain: Behavioural expression and response in an evolutionary framework

An evolutionary perspective offers insights into the major public health problem of chronic (persistent) pain; behaviours associated with it perpetuate both pain and disability. Pain is motivating, and pain-related behaviours promote recovery by immediate active or passive defence; subsequent protection of wounds; suppression of competing responses; energy conservation; vigilance to threat; and learned avoidance of associated cues. When these persist beyond healing, as in chronic pain, they are disabling. In mammals, facial and bodily expression of pain is visible and identifiable by others, while social context, including conspecifics' responses, modulate pain. Studies of responses to pain emphasize onlooker empathy, but people with chronic pain report feeling disbelieved and stigmatized. Observers frequently discount others' pain, best understood in terms of cheater detection-alertness to free riders that underpins the capacity for prosocial behaviours. These dynamics occur both in everyday life and in clinical encounters, providing an account of the adaptiveness of pain-related behaviours.

Intervertebral disc degeneration and how it leads to low back pain

The purpose of this review was to evaluate data generated by animal models of intervertebral disc (IVD) degeneration published in the last decade and show how this has made invaluable contributions to the identification of molecular events occurring in and contributing to pain generation. IVD degeneration and associated spinal pain is a complex multifactorial process, its complexity poses difficulties in the selection of the most appropriate therapeutic target to focus on of many potential candidates in the formulation of strategies to alleviate pain perception and to effect disc repair and regeneration and the prevention of associated neuropathic and nociceptive pain. Nerve ingrowth and increased numbers of nociceptors and mechanoreceptors in the degenerate IVD are mechanically stimulated in the biomechanically incompetent abnormally loaded degenerate IVD leading to increased generation of low back pain. Maintenance of a healthy IVD is, thus, an important preventative measure that warrants further investigation to preclude the generation of low back pain. Recent studies with growth and differentiation factor 6 in IVD puncture and multi-level IVD degeneration models and a rat xenograft radiculopathy pain model have shown it has considerable potential in the prevention of further deterioration in degenerate IVDs, has regenerative properties that promote recovery of normal IVD architectural functional organization and inhibits the generation of inflammatory mediators that lead to disc degeneration and the generation of low back pain. Human clinical trials are warranted and eagerly anticipated with this compound to assess its efficacy in the treatment of IVD degeneration and the prevention of the generation of low back pain.

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 neurobiology of pain and facial movements in rodents: Clinical applications and current research

One of the most controversial aspects of the use of animals in science is the production of pain. Pain is a central ethical concern. The activation of neural pathways involved in the pain response has physiological, endocrine, and behavioral consequences, that can affect both the health and welfare of the animals, as well as the validity of research. The strategy to prevent these consequences requires understanding of the nociception process, pain itself, and how assessment can be performed using validated, non-invasive methods. The study of facial expressions related to pain has undergone considerable study with the finding that certain movements of the facial muscles (called facial action units) are associated with the presence and intensity of pain. This review, focused on rodents, discusses the neurobiology of facial expressions, clinical applications, and current research designed to better understand pain and the nociceptive pathway as a strategy for implementing refinement in biomedical research.

The Neuroimmune Interface and Chronic Pain Through the Lens of Production Animals

Communication between the central nervous system (CNS) and the immune system has gained much attention for its fundamental role in the development of chronic and pathological pain in humans and rodent models. Following peripheral nerve injury, neuroimmune signaling within the CNS plays an important role in the pathophysiological changes in pain sensitivity that lead to chronic pain. In production animals, routine husbandry procedures such as tail docking and castration, often involve some degree of inflammation and peripheral nerve injury and consequently may lead to chronic pain. Our understanding of chronic pain in animals is limited by the difficulty in measuring this pathological pain state. In light of this, we have reviewed the current understanding of chronic pain in production animals. We discuss our ability to measure pain and the implications this has on animal welfare and production outcomes. Further research into the neuroimmune interface in production animals will improve our fundamental understanding of chronic pain and better inform human clinical pain management and animal husbandry practices and interventions.

Measurement properties of grimace scales for pain assessment in non-human mammals: a systematic review

ABSTRACT

Facial expressions of pain have been identified in several animal species. The aim of this systematic review was to provide evidence on the measurement properties of grimace scales for pain assessment. The protocol was registered (SyRF#21-Nov-2019) and the study is reported according to the PRISMA guidelines. Studies reporting the development, validation, and the assessment of measurement properties of grimace scales were included. Data extraction and assessment were performed by two investigators, following the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines. Six categories of measurement properties were assessed: internal consistency, reliability, measurement error, criterion and construct validity, and responsiveness. Overall strength of evidence (high, moderate, low) of each instrument was based on methodological quality, number of studies and studies' findings. Twelve scales for nine species were included (mice, rats, rabbits, horses, piglets, sheep/lamb, ferrets, cats and donkeys). Considerable variability regarding their development and measurement properties was observed. The Mouse, Rat, Horse and Feline Grimace Scales exhibited high level of evidence. The Rabbit, Lamb, Piglet and Ferret Grimace Scales and Sheep Pain Facial Expression Scale exhibited moderate level of evidence. The Sheep Grimace Scale, EQUUS-FAP and EQUUS-Donkey-FAP exhibited low level of evidence for measurement properties. Construct validity was the most reported measurement property. Reliability and other forms of validity have been understudied. This systematic review identified gaps in knowledge on the measurement properties of grimace scales. Further studies should focus on improving psychometric testing, instrument refinement and the use of grimace scales for pain assessment in non-human mammals.

Does Smart Farming Improve or Damage Animal Welfare? Technology and What Animals Want

“Smart” or “precision” farming has revolutionized crop agriculture but its application to livestock farming has raised ethical concerns because of its possible adverse effects on animal welfare. With rising public concern for animal welfare across the world, some people see the efficiency gains offered by the new technology as a direct threat to the animals themselves, allowing producers to get “more for less” in the interests of profit. Others see major welfare advantages through life-long health monitoring, delivery of individual care and optimization of environmental conditions. The answer to the question of whether smart farming improves or damages animal welfare is likely to depend on three main factors. Firstly, much will depend on how welfare is defined and the extent to which politicians, scientists, farmers and members of the public can agree on what welfare means and so come to a common view on how to judge how it is impacted by technology. Defining welfare as a combination of good health and what the animals themselves want provides a unifying and animal-centered way forward. It can also be directly adapted for computer recognition of welfare. A second critical factor will be whether high welfare standards are made a priority within smart farming systems. To achieve this, it will be necessary both to develop computer algorithms that can recognize welfare to the satisfaction of both the public and farmers and also to build good welfare into the control and decision-making of smart systems. What will matter most in the end, however, is a third factor, which is whether smart farming can actually deliver its promised improvements in animal welfare when applied in the real world. An ethical evaluation will only be possible when the new technologies are more widely deployed on commercial farms and their full social, environmental, financial and welfare implications become apparent.

Preference and discrimination of facial expressions of humans, rats, and mice by C57 mice

Social animals likely recognize emotional expressions in other animals. Recent studies suggest that mice can visually perceive emotional expressions of other mice. In the first experiment, we measured the preference of mice for two different facial expressions (a normal facial expression and an expression of negative emotion such as pain) of rats, mice, and humans. Results revealed that mice showed a slight preference for the normal expression over the face expressing pain in the case of rats, but no preference in the case of others. In the second experiment, we trained mice to discriminate between the two facial expressions in an operant chamber with a touch screen. They could discriminate facial expressions of mice and rats, but they did not show discrimination of human facial expressions. Principal component analysis of the images of stimuli reveals negative correlation between pixel-based dissimilarity of training stimuli and the number of sessions to criterion. The mice showed generalization to novel images of the mouse faces with and without pain but did not maintain their discriminative behavior when new rat faces were shown. These results suggest that mice display category discrimination of conspecific facial expressions but not of other species.

The Use of Artificial Intelligence in Assessing Affective States in Livestock

In order to promote the welfare of farm animals, there is a need to be able to recognize, register and monitor their affective states. Numerous studies show that just like humans, non-human animals are able to feel pain, fear and joy amongst other emotions, too. While behaviorally testing individual animals to identify positive or negative states is a time and labor consuming task to complete, artificial intelligence and machine learning open up a whole new field of science to automatize emotion recognition in production animals. By using sensors and monitoring indirect measures of changes in affective states, self-learning computational mechanisms will allow an effective categorization of emotions and consequently can help farmers to respond accordingly. Not only will this possibility be an efficient method to improve animal welfare, but early detection of stress and fear can also improve productivity and reduce the need for veterinary assistance on the farm. Whereas affective computing in human research has received increasing attention, the knowledge gained on human emotions is yet to be applied to non-human animals. Therefore, a multidisciplinary approach should be taken to combine fields such as affective computing, bioengineering and applied ethology in order to address the current theoretical and practical obstacles that are yet to be overcome.

Pain Management in Farm Animals: Focus on Cattle, Sheep and Pigs

Pain causes behavioral, autonomic, and neuroendocrine changes and is a common cause of animal welfare compromise in farm animals. Current societal and ethical concerns demand better agricultural practices and improved welfare for food animals. These guidelines focus on cattle, sheep, and pigs, and present the implications of pain in terms of animal welfare and ethical perspectives, and its challenges and misconceptions. We provide an overview of pain management including assessment and treatment applied to the most common husbandry procedures, and recommendations to improve animal welfare in these species. A cost-benefit analysis of pain mitigation is discussed for food animals as well as the use of pain scoring systems for pain assessment in these species. Several recommendations are provided related to husbandry practices that could mitigate pain and improve farm animal welfare. This includes pain assessment as one of the indicators of animal welfare, the use of artificial intelligence for automated methods and research, and the need for better/appropriate legislation, regulations, and recommendations for pain relief during routine and husbandry procedures.

Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare

The rise in the demand for animal products due to demographic and dietary changes has exacerbated difficulties in addressing societal concerns related to the environment, human health, and animal welfare. As a response to this challenge, Precision Livestock Farming (PLF) technologies are being developed to monitor animal health and welfare parameters in a continuous and automated way, offering the opportunity to improve productivity and detect health issues at an early stage. However, ethical concerns have been raised regarding their potential to facilitate the management of production systems that are potentially harmful to animal welfare, or to impact the human-animal relationship and farmers' duty of care. Using the Five Domains Model (FDM) as a framework, the aim is to explore the potential of PLF to help address animal welfare and to discuss potential welfare benefits and risks of using such technology. A variety of technologies are identified and classified according to their type [sensors, bolus, image or sound based, Radio Frequency Identification (RFID)], their development stage, the species they apply to, and their potential impact on welfare. While PLF technologies have promising potential to reduce the occurrence of diseases and injuries in livestock farming systems, their current ability to help promote positive welfare states remains limited, as technologies with such potential generally remain at earlier development stages. This is likely due to the lack of evidence related to the validity of positive welfare indicators as well as challenges in technology adoption and development. Finally, the extent to which welfare can be improved will also strongly depend on whether management practices will be adapted to minimize negative consequences and maximize benefits to welfare.

Measuring Farm Animal Emotions-Sensor-Based Approaches

Understanding animal emotions is a key to unlocking methods for improving animal welfare. Currently there are no 'benchmarks' or any scientific assessments available for measuring and quantifying the emotional responses of farm animals. Using sensors to collect biometric data as a means of measuring animal emotions is a topic of growing interest in agricultural technology. Here we reviewed several aspects of the use of sensor-based approaches in monitoring animal emotions, beginning with an introduction on animal emotions. Then we reviewed some of the available technological systems for analyzing animal emotions. These systems include a variety of sensors, the algorithms used to process biometric data taken from these sensors, facial expression, and sound analysis. We conclude that a single emotional expression measurement based on either the facial feature of animals or the physiological functions cannot show accurately the farm animal's emotional changes, and hence compound expression recognition measurement is required. We propose some novel ways to combine sensor technologies through sensor fusion into efficient systems for monitoring and measuring the animals' compound expression of emotions. Finally, we explore future perspectives in the field, including challenges and opportunities.

The Utility of Grimace Scales for Practical Pain Assessment in Laboratory Animals

Simple Summary

Grimace scales for laboratory animals were first reported ten years ago. Yet, despite their promise as pain assessment tools it appears that they have not been implemented widely in animal research establishments for clinical pain assessment. We discuss potential reasons for this based on the knowledge gained to date on their use and suggest avenues for further research, which might improve uptake of their use in laboratory animal medicine.

Abstract

Animals’ facial expressions are widely used as a readout for emotion. Scientific interest in the facial expressions of laboratory animals has centered primarily on negative experiences, such as pain, experienced as a result of scientific research procedures. Recent attempts to standardize evaluation of facial expressions associated with pain in laboratory animals has culminated in the development of “grimace scales”. The prevention or relief of pain in laboratory animals is a fundamental requirement for in vivo research to satisfy community expectations. However, to date it appears that the grimace scales have not seen widespread implementation as clinical pain assessment techniques in biomedical research. In this review, we discuss some of the barriers to implementation of the scales in clinical laboratory animal medicine, progress made in automation of collection, and suggest avenues for future research.