Influence of seating styles on head and pelvic vertical movement symmetry in horses ridden at trot

Detailed knowledge of how a rider’s seating style and riding on a circle influences the movement symmetry of the horse’s head and pelvis may aid rider and trainer in an early recognition of low grade lameness. Such knowledge is also important during both subjective and objective lameness evaluations in the ridden horse in a clinical setting. In this study, inertial sensors were used to assess how different rider seating styles may influence head and pelvic movement symmetry in horses trotting in a straight line and on the circle in both directions. A total of 26 horses were subjected to 15 different conditions at trot: three unridden conditions and 12 ridden conditions where the rider performed three different seating styles (rising trot, sitting trot and two point seat). Rising trot induced systematic changes in movement symmetry of the horses. The most prominent effect was decreased pelvic rise that occurred as the rider was actively rising up in the stirrups, thus creating a downward momentum counteracting the horses push off. This mimics a push off lameness in the hindlimb that is in stance when the rider sits down in the saddle during the rising trot. On the circle, the asymmetries induced by rising trot on the correct diagonal counteracted the circle induced asymmetries, rendering the horse more symmetrical. This finding offers an explanation to the equestrian tradition of rising on the ‘correct diagonal.’ In horses with small pre-existing movement asymmetries, the asymmetry induced by rising trot, as well as the circular track, attenuated or reduced the horse’s baseline asymmetry, depending on the sitting diagonal and direction on the circle. A push off hindlimb lameness would be expected to increase when the rider sits during the lame hindlimb stance whereas an impact hindlimb lameness would be expected to decrease. These findings suggest that the rising trot may be useful for identifying the type of lameness during subjective lameness assessment of hindlimb lameness. This theory needs to be studied further in clinically lame horses

Effect of meloxicam treatment on movement asymmetry in riding horses in training

Quantitative gait analysis has revealed that a large proportion of horses in training, perceived as free from lameness by their owners, show movement asymmetries of equal magnitude to horses with mild clinical lameness. Whether these movement asymmetries are related to orthopaedic pain and/or pathology has yet to be further investigated. Therefore, the objective of this study was to determine whether movement asymmetries in riding horses in training are affected by anti-inflammatory treatment with meloxicam. In a crossover design, horses were treated with meloxicam or placebo for four days respectively, with a 14–16 day washout period between treatments. Objective movement analysis utilising body mounted accelerometers was performed on a hard and a soft surface before and on day four of each treatment. A trial mean was calculated for the differences between the two vertical displacement minima and maxima of head (HDmin, HDmax) and pelvis (PDmin, PDmax) per stride. Horses (n = 66) with trial mean asymmetries greater than 6 mm for HDmin or HDmax, or more than 3 mm for PDmin or PDmax, at baseline were included. The difference before and after each treatment in the measured movement asymmetry was assessed with linear mixed models. Treatment with meloxicam did not significantly affect the movement asymmetry in any of the models applied (all p>0.30). These results raise new questions: are the movement asymmetries in riding horses in training simply expressions of biological variation or are they related to pain/dysfunction that is non-responsive to meloxicam treatment

Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning

For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in horses are warranted. In this study, we used a full body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait. Using data from 120 horses of four different domestic breeds, equipped with seven motion sensors, we included 7576 strides from eight different gaits. GC was trained using several machine-learning approaches, both from feature-extracted data and from raw sensor data. Our best GC model achieved 97% accuracy. Our technique facilitated accurate, GC that enables in-depth biomechanical studies and allows for highly accurate phenotyping of gait for genetic research and breeding. Our approach lends itself for potential use in other quadrupedal species without the need for developing gait/animal specific algorithms.status: publishe

Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning

For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in horses are warranted. In this study, we used a full body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait. Using data from 120 horses of four different domestic breeds, equipped with seven motion sensors, we included 7576 strides from eight different gaits. GC was trained using several machine-learning approaches, both from feature-extracted data and from raw sensor data. Our best GC model achieved 97% accuracy. Our technique facilitated accurate, GC that enables in-depth biomechanical studies and allows for highly accurate phenotyping of gait for genetic research and breeding. Our approach lends itself for potential use in other quadrupedal species without the need for developing gait/animal specific algorithms

Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning

For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in horses are warranted. In this study, we used a full body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait. Using data from 120 horses of four different domestic breeds, equipped with seven motion sensors, we included 7576 strides from eight different gaits. GC was trained using several machine-learning approaches, both from feature-extracted data and from raw sensor data. Our best GC model achieved 97% accuracy. Our technique facilitated accurate, GC that enables in-depth biomechanical studies and allows for highly accurate phenotyping of gait for genetic research and breeding. Our approach lends itself for potential use in other quadrupedal species without the need for developing gait/animal specific algorithms