Effect of transportation and social isolation on facial expressions of healthy horses

Abstract

Horses have the ability to generate a remarkable repertoire of facial expressions, some of which have been linked to the affective component of pain. This study describes the facial expressions in healthy horses free of pain before and during transportation and social isolation, which are putatively stressful but ordinary management procedures. Transportation was performed in 28 horses by subjecting them to short-term road transport in a horse trailer. A subgroup (n = 10) of these horses was also subjected to short-term social isolation. During all procedures, a body-mounted, remote-controlled heart rate monitor provided continuous heart rate measurements. The horses’ heads were video-recorded during the interventions. An exhaustive dataset was generated from the selected video clips of all possible facial action units and action descriptors, time of emergency, duration, and frequency according to the Equine Facial Action Coding System (EquiFACS). Heart rate increased during both interventions (p<0.01), confirming that they caused disruption in sympato-vagal balance. Using the current method for ascribing certain action units (AUs) to specific emotional states in humans and a novel data-driven co-occurrence method, the following facial traits were observed during both interventions: 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). Increases in ‘ear flicker’ (p<0.001) and blink frequency (p<0.001) were also seen. These facial actions were used to train a machine-learning classifier to discriminate between the high-arousal interventions and calm horses, which achieved at most 79% accuracy. Most facial features identified correspond well with previous findings on behaviors of stressed horses, for example flared nostrils, repetitive mouth behaviors, increased eye white, tongue show, and ear movements. Several features identified in this study of pain-free horses, such as dilated nostrils, eye white increase, and inner brow raiser, are used as indicators of pain in some face-based pain assessment tools. In order to increase performance parameters in pain assessment tools, the relations between facial expressions of stress and pain should be studied further.

Objective Assessment of Chronic Pain in Horses Using the Horse Chronic Pain Scale (HCPS): A Scale-Construction Study

“…Therefore, other emotional states, induced by the presence of the observers, may also have influenced the facial expressions in the horses of the current study. In a recent publication [ 22 ], the effect of stressors such as transportation or social isolation of horses on facial expressions was described as well. Another possible explanation could be that the relatively mild chronic conditions in the patient group may have led to minimal increases in facial expressions of pain.…”

Towards Machine Recognition of Facial Expressions of Pain in Horses

“…When we used the co-occurrence graph to determine the conjoined pain AUs, we saw that more AUs of the lower face were identified as indicative of pain, including the chin raiser (AU17), nostril dilator (AD38) and chewing action (AD81) identified previously and also the lip pucker (AU18) and upper lip raiser (AU10). On applying the same statistical methods to sound horses subjected to stressful interventions [52], we observed increased frequencies of eye white increase (AD1), nostril dilator (AD38), upper eyelid raiser (AU5), inner brow raiser (AU101) and tongue show (AD19), along with an increase in "ear flicker" and "blink frequency". These results show that ML can be successfully applied on FACS data for horses to reveal more distinct interpretations of the affective states of pain and stress.…”

“…The FACS standard has been widely adopted by the human research community owing to the exhaustive nature of FACS descriptions [47][48][49][50] and to the fact that FACS can code all possible movements of the face and not only predetermined expressions. The FACS standard for horses, EquiFACS, was developed in 2015 [44] but has only recently been used for the investigation of affective states such as pain [51] and emotional stress [52] in horses. Manual FACS is not suitable as a clinical tool because it requires frame-by-frame coding of video sequences by a trained and certified FACS reader, and is thus extremely resource-demanding, with coding time requirements at least in the range of 1:100 for the average video, one second of video requiring 100 secs of annotation time.…”

“…The method also does not define AUs or ADs that might be rare, but important, for pain detection in the horse. We, therefore, developed graph-based statistical methods that describe the co-occurrence of AUs and methods for detecting AUs that co-occur (conjoined AUs) over varying periods of time [51,52]. A more complex picture emerged when this co-occurrence method was applied.…”

“…With the promising results from the manually annotated EquiFACS datasets [51,52], we investigated methods for automated recognition of horse facial AUs in still images [110]. Previous work has explored automated detection of keypoint-based facial expression information, but in a simplified form compared to, for example, EquiFACS [111][112][113].…”