Search Results (6)

The Ridden Horse Pain Ethogram (RHpE) comprising 24 behaviours was developed to facilitate the identification of musculoskeletal discomfort, with scores of ≥8/24 indicating the presence of pain. The median RHpE score for 147 competitors at World Cup Grand Prix events from 2018 to 2020 was three (interquartile range [IQR] 1–4; range 0–7). The aim of the current study was to apply the RHpE to 38 competitors at the Hickstead-Rotterdam Grand Prix Challenge and 26 competitors at the British Dressage Grand Prix National Championship in 2020. The median RHpE scores were four (IQR 3–6; range 0–8) and six (IQR 4–7; range 1–9), respectively, which were both higher (p = 0.0011 and p = 0.0000) than the World Cup competitors’ scores. Ears back ≥ 5 s (p = 0.005), intense stare ≥ 5 s (p = 0.000), repeated tail swishing (p = 0.000), hindlimb toe drag (p = 0.000), repeated tongue-out (p = 0.003) and crooked tail-carriage (p = 0.000) occurred more frequently. These were associated with a higher frequency of lameness, abnormalities of canter, and errors in rein-back, passage and piaffe, canter flying-changes and canter pirouettes compared with World Cup competitors. There was a moderate negative correlation between the dressage judges’ scores and the RHpE scores (Spearman’s rho −0.66, p = 0.0002) at the British Championship. Performance and welfare may be improved by recognition and appropriate treatment of underlying problems. Full article

There is considerable debate about the social license to compete with horses and controversy about training methods for dressage horses. The objectives were to: 1. apply the Ridden Horse Pain Ethogram (RHpE) to dressage horses competing at elite Grand Prix level; 2. compare RHpE and judges’ scores; and 3. document deviations in gaits from Fédération Equestre Internationale (FEI) guidelines. Video recordings of 147 competitors from nine World Cup competitions were assessed. Spearman’s rank correlation coefficient tested the correlation between RHpE and judges’ scores. The median RHpE score was 3 (IQR 1, 4; range 0, 7). There was a moderate negative correlation (Spearman rho −0.40, p < 0.001) between the RHpE scores and the judges’ scores. Mouth open with separation of the teeth for ≥10 s (68%), head behind vertical ≥10° ≥10 s (67%), an intense stare for ≥5 s (30%) and repeated tail swishing (29%) were the most frequent RHpE behaviours. Deviations from FEI guidelines were most frequent in passage, piaffe, canter flying-changes, canter pirouettes and “halt-immobility-rein back five steps-collected trot”. In conclusion, most horses appeared to work comfortably for the majority of the test. Further investigation of the influence of a double bridle compared with a snaffle bridle on head position and mouth opening is merited. Full article

The piaffe is an artificial, diagonally coordinated movement performed in the highest levels of dressage competition. The ground reaction forces (GRFs) of horses performing the piaffe do not appear to have been reported. Therefore, the objective of this study was to describe three-dimensional GRFs in ridden dressage horses performing the piaffe. In-ground force plates were used to capture fore and hindlimb GRF data from seven well-trained dressage horses. Peak vertical GRF was significantly higher in forelimbs than in the hindlimbs (7.39 ± 0.99 N/kg vs. 6.41 ± 0.64 N/kg; p < 0.001) with vertical impulse showing a trend toward higher forelimb values. Peak longitudinal forces were small with no difference in the magnitude of braking or propulsive forces between fore and hindlimbs. Peak transverse forces were similar in magnitude to longitudinal forces and were mostly directed medially in the hindlimbs. Both the intra- and inter-individual variability of longitudinal and transverse GRFs were high (coefficient of variation 25–68%). Compared with the other diagonal gaits of dressage horses, the vertical GRF somewhat shifted toward the hindlimbs. The high step-to-step variability of the horizontal GRF components is thought to reflect the challenge of balancing on one diagonal pair of limbs with no forward momentum. Full article

Gaits are typically classified as walking or running based on kinematics, the shape of the vertical ground reaction force (GRF) curve, and the use of inverted pendulum or spring-mass mechanics during the stance phase. The objectives of this review were to describe the biomechanical characteristics that differentiate walking and running gaits, then apply these criteria to classify and compare the enhanced natural gait of collected trot with the artificial gaits of passage and piaffe as performed by highly trained dressage horses. Limb contact and lift off times were used to determine contact sequence, limb phase, duty factor, and aerial phase duration. Ground reaction force data were plotted to assess fore and hind limb loading patterns. The center of mass (COM) trajectory was evaluated in relation to changes in potential and kinetic energy to assess the use of inverted pendulum and spring-mass mechanics. Collected trot and passage were classified as running gaits according to all three criteria whereas piaffe appears to be a hybrid gait combining walking kinematics with running GRFs and COM mechanics. The hind limbs act as springs and show greater limb compression in passage and piaffe compared with trot, whereas the forelimbs behave more like struts showing less compression in passage and piaffe than in trot. Full article

Ultra-wideband radar-based penetrating detection and recognition of human activities has become a focus on remote sensing in various military applications in recent years, such as urban warfare, hostage rescue, and earthquake post-disaster rescue. However, an excellent micro-Doppler signature (MDS) extracting method of human motion with high time-frequency resolution, outstanding anti-interference ability, and extensive adaptability, which aims to provide favorable and more detailed features for human activity recognition and classification, especially in the non-free space detection environment, is in great urgency. To cope with the issue, a multiple Hilbert-Huang transform (MHHT) method is proposed for high-resolution time-frequency analysis of finer-grained human activity MDS hidden in ultra-wideband (UWB) radar echoes during the through-wall detection environment. Based on the improved HHT with effective intrinsic mode function (IMF) selection according to the cosine similarity (CS) principle, the improved HHT is applied to each channel signal in the effective channel scope of the UWB radar signal and then integrated along the range direction. The activities of swinging one or two arms while standing at a spot 3 m from a wall were used to validate the abilities of the proposed method for extracting and separating the MDS of different moving body structures with a high time-frequency resolution. Simultaneously, the corresponding relationship between the frequency components in MHHT-based spectra and structures of the moving human body was demonstrated according to the radar Doppler principle combined with the principle of human body kinematics. Moreover, six common finer-grained human activities and a piaffe at different ranges under the through-wall detection environment were exploited to confirm the adaptability of the novel method for different activities and pre-eminent anti-interference ability under a low signal-noise-clutter ratio (SNCR) environment, which is critical for remote sensing in various military application, such as urban warfare, hostage rescue, earthquake post-disaster rescue. Full article

The through-wall detection and classification of human activities are critical for anti-terrorism, security, and disaster rescue operations. An effective through-wall detection and classification technology is proposed for finer-grained human activities such as piaffe, picking up an object, waving, jumping, standing with random micro-shakes, and breathing while sitting. A stepped-frequency continuous wave (SFCW) bio-radar sensor is first used to conduct through-wall detection of finer-grained human activities; Then, a comprehensive range accumulation time-frequency transform (CRATFR) based on inverse weight coefficients is proposed, which aims to strengthen the micro-Doppler features of finer activity signals. Finally, in combination with the effective eigenvalues extracted from the CRATFR spectrum, an optimal self-adaption support vector machine (OS-SVM) based on prior human position information is introduced to classify different finer-grained activities. At a fixed position (3 m) behind a wall, the classification accuracies of six activities performed by eight individuals were 98.78% and 93.23%, respectively, for the two scenarios defined in this paper. In the position-changing experiment, an average classification accuracy of 86.67% was obtained for five finer-grained activities (excluding breathing) of eight individuals within 6 m behind the wall for the most practical scenario, a significant improvement over the 79% accuracy of the current method. Full article