Abstract
Simple SummaryAnalyzing equestrian show jumping and dressage training movements can be greatly useful during training, but existing technologies fall short in terms of user convenience and detection of major horse training activities. As a result, attaching sensors to the horse’s legs could give a simple solution that is accessible to all riders. However, there is a scarcity of research on automatic classification of horse jumping and dressage training movements. Thus, the goal of this study was to use an advanced machine learning algorithm to categorize leg accelerometer data from the majority of dressage and jumping training motions. This is the first study to show that jumping and dressage training movements can be accurately identified and the velocity of different gaits and paces can be estimated with a minimal error.Equine training activity detection will help to track and enhance the performance and fitness level of riders and their horses. Currently, the equestrian world is eager for a simple solution that goes beyond detecting basic gaits, yet current technologies fall short on the level of user friendliness and detection of main horse training activities. To this end, we collected leg accelerometer data of 14 well-trained horses during jumping and dressage trainings. For the first time, 6 jumping training and 25 advanced horse dressage activities are classified using specifically developed models based on a neural network. A jumping training could be classified with a high accuracy of 100 %, while a dressage training could be classified with an accuracy of 96.29%. Assigning the dressage movements to 11, 6 or 4 superclasses results in higher accuracies of 98.87%, 99.10% and 100%, respectively. Furthermore, during dressage training, the side of movement could be identified with an accuracy of 97.08%. In addition, a velocity estimation model was developed based on the measured velocities of seven horses performing the collected, working, and extended gaits during a dressage training. For the walk, trot, and canter paces, the velocities could be estimated accurately with a low root mean square error of 0.07 m/s, 0.14 m/s, and 0.42 m/s, respectively.
Highlights
Horse riding has become increasingly popular, and there is ample evidence that riders derive a lot of pleasure and enjoyment from engaging in various equestrian disciplines [1].The most popular equestrian events are Olympic-level disciplines, i.e., show jumping and dressage [2]
This variety of horses is appropriate for our research as the variation in levels will contribute to the generalization of the machine learning model, as accelerometer data patterns will be different for different disciplines and levels
Flying changes are considered as an important jumping training activity and we added data of three dressage horses performing nineteen flying changes to test the model’s performance.Because the data is measured at 50 Hz and the optimal sampling rate in our previous work [14] was set at 10 Hz, the dataset is sub-sampled at this rate
Summary
Horse riding has become increasingly popular, and there is ample evidence that riders derive a lot of pleasure and enjoyment from engaging in various equestrian disciplines [1]. The most popular equestrian events are Olympic-level disciplines, i.e., show jumping and dressage [2]. During training and competition a jumping horse completes a course with fences and a dressage horse performs different predefined gaits, patterns and movements. Detecting those movements during a riding session provides insights into training and is needed to quantify the type and intensity of training sessions. Analyzing locomotion data could provide insights in horses’ fitness level and define horse qualities. Dressage horses that were higher placed in the Olympic Games had higher speeds in the extended canter due to taking longer strides [4]
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