Facial expressions of emotional stress in horses

Abstract

Horses have the ability to generate a remarkable repertoire of facial expressions, some which have been linked to certain emotional states, for example pain. Studies suggest that facial expressions may be a more ‘honest’ expression of emotional state in horses than behavioral or physiological parameters. This study sought to describe the facial expressions during stress of healthy horses free of pain, using a standardized method of recording facial expressions in video. Stress was induced in 28 horses by subjecting them to road transport and 10 of these horses were also subjected to social isolation. The horses served as their own control. A body-mounted, remote controlled heart rate monitor provided continuous heart rate measurements during the interventions. The horses’ facial expressions were video-recorded during the interventions. Frequency and duration of each facial expression were then determined, according to the Equine Facial Action Coding System. Heart rate increased during the stressful interventions (p=0.01), confirming that the interventions were stressful. Using both the human investigation- and the co-occurrence methods, the following facial traits could be observed during stress: eye white increase (p<0.001), nostril dilator (p<0.001), upper eyelid raiser (p<0.001), inner brow raiser (p=0.042), tongue show (p<0.001) along with an increase in ‘ear flicker’ (p<0.001) and blink frequency (p<0.001). The facial actions were successfully used to train a machine-learning classifier to discriminate between stressed and calm horses, with an accuracy of 74.2 %. Most of the facial features identified correspond well with previous research on the subject, for example flared nostrils, repetitive mouth behaviors, increased eye white, tongue show and ear movements. Some features selected as indicative of emotional pain-free stress are used in face-based pain assessment tools, such as dilated nostrils, eye white increase or inner brow raiser. The relation between facial expressions of stress and pain should therefore further be studied.

Improving the Reliability of Scale-Free Image Morphometrics in Applications with Minimally Restrained Livestock Using Projective Geometry and Unsupervised Machine Learning

“…From a more pragmatic standpoint, the head is highly mobile with respect to all three axes of movement (pitch, roll, and yaw) even while the cow is otherwise stationary in a headlock, which facilitated location of a large number of individual animals in a working farm environment for repeated sampling. Finally, while comparisons between measurement systems are intended to be readily generalizable to a broader scope of prospective use cases, there are emerging utilitarian interests in assessment of livestock faces to extract measures of facial expression [ 26 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ] and morphology [ 27 , 34 ]. The authors hope that this work may provide more direct methodological insights for the inevitable progression of this growing body of research towards objective image-based measurements.…”

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

“…This would require a more robust tracking of the horse in the video, for instance using animal pose estimation methods such as [7,27]. Learning to separate stress from pain is another important but difficult avenue to consider [25]. 6 in the main article).…”