Much controversy exists among riders, and in particular among those practicing dressage, regarding what can be considered an “appropriate” Head-Neck-Position (HNP). The objective was to assess the prevalence of different HNPs in the field, the behavioural reactions of horses during warm-up and competition rides in relation to HNP and the relation between HNP and marks achieved in the competition. Horses (n = 171) were selected during dressage competitions according to their HNP (3 categories based on the degree of flexion), and their behaviour was recorded during 3 minutes each of riding in the warm-up area and in the competition. Scans were carried out on an additional 355 horses every 15 minutes to determine the proportion of each HNP in the warm-up area. Sixty-nine percent of the 355 horses were ridden with their nasal planes behind the vertical in the warm-up area, 19% were ridden at or behind the vertical and only 12% were ridden with their nasal plane in front of the vertical. Horses carrying their nasal plane behind the vertical exhibited significantly (P<0.0001) more conflict behaviours than horses with their nose held in front of the vertical. Horses were commonly presented with a less flexed HNP during competition compared to warm-up (P<0.05). A HNP behind the vertical was penalised with lower marks in the lower (P = 0.0434) but not in the higher (P = 0.9629) competition levels. Horses in higher classes showed more (P = 0.0015) conflict behaviour than those in lower classes. In conclusion, dressage horses are commonly ridden during warm-up for competitions with their nasal plane behind the vertical, and this posture seems to cause significantly more conflict behaviour than HNPs in front of the vertical.

Too much rein tension while riding may compromise the welfare of the horse. But who generates the tension on the reins—the horse or the rider? The primary aim of this pilot study was to evaluate the maximum rein tension that horses voluntarily maintain without a rider compared to rein tension with a rider. A secondary aim was to evaluate conflict behaviours in relation to rein tension. Thirteen horses were used, all fitted with customised “Animon” rein tension sensors (25 Hz, up to 600 N range), free-moving with side reins set in dressage competition frame with the noseline on the vertical. Rein tension was measured at the walk, trot, and canter in both directions in a round pen. The same horses were then ridden by their usual riders and completed the same task on a riding ground. Continuous video recordings were obtained to subsequently quantify the occurrence of conflict behaviours. The difference in mean maximum peak of rein tension with and without a rider for each gait was compared using the Wilcoxon Rank Sum test. Without a rider, rein tension was significantly lower (Wilcoxon T = 0, p < 0.01, 7.5 N ± 2.8 N) than with a rider (Wilcoxon T = 0, p < 0.01, 24.0 N ± 12.3 N). Regardless of the different rein tensions in the ridden exercise, all of the horses exhibited approximately the same amount of rein tension in the unridden exercise. The frequency of conflict behaviour was higher with a rider than without (11 ± 14 per minute vs. 2 ± 3 per minute; T = 4, p < 0.01).

This paper focuses on the automated analysis of motion patterns for relating an individual's behaviour with its pain experience. Reliable, automated behaviour analysis can improve video observation considerably, i.e. by lessening the work load of human operators, decreasing human error and by increasing anonymity and privacy. Possible applications are observation of traffic, public places and public transport. A new potential application is the early detection of pathologically relevant events, e.g. animal diseases (e.g. colics in horses), the automated post-surgical pain assessment of animals and similar applications. The challenge in the horses scenario is that they are flight animals that can not afford much of visible pain behaviour.Our approach is built on top of state of the art methods for object detection and tracking. From the object motion we derive motion patterns and respective features which we analyse by machine-learning methods. In the this paper we will present atypical behaviour detection (i.e. pain estimation) in animal videos, for which we have acquired a large video database. It could be shown that the condition of the horse can be analysed and classified by means of local histograms.

Horse sports face public scrutiny, particularly regarding concerns about horse welfare. Understanding how the physical positioning of the horse's head and neck while being ridden affects behavior is crucial for improving welfare standards. This study aimed to investigate the influence of head–neck position (HNP) in ridden horses–specifically, the vertical and poll angles–on conflict behavior (CB). Furthermore, it aimed to identify a critical threshold at which HNP significantly affects behavioral outcomes. Elite dressage horses were observed during warm-ups and tests at competitions, and horses presented in educational videos were analyzed to examine the relationship between HNP and CB. A total of 191 rides were analyzed, HNP parameters (angle to the vertical of nasal plane, poll angle, and shoulder angle) and CB indicators (e.g., unusual oral behaviors and tail swishing) were recorded over 3-minute intervals using Observer XT (Noldus). Data were analyzed using general linear mixed-effects models, with CB (sum of all behaviors) as the response variable and horse-rider-ID as a random effect. Both poll and vertical angles had significant negative effects on CB, indicating that horses exhibited more CB when these angles decreased (p < 0.001, df = 139.64/137.28, respectively). Shoulder angle also had a significant impact on CB in one model (p = 0.014, df = 136.61). A vertical angle of -7.5° behind the vertical was identified as the cut-off value associated with an increase in CB. Stallions exhibited more CB than mares and geldings (p < 0.001, df = 73.16). The models demonstrated good fit, with consistently non-significant effects of age, breed, and bit type. Optimizing HNP during riding is crucial for enhancing behavioral well-being and the overall welfare of dressage horses in competitive settings.