Classification of the different movements (walk/trot/canter) anddata collection of pose estimation

Abstract: Pose estimation uses computer vision to predict how a body moves. The likeliness off different movements is predicted with a neural network and the most likely pose is predicted. With DeepLabCut, an open source software package for 3D animal pose estimation, information about animals behaviour and movement can be extracted. In this report the pose estimation of horses four hooves is used. By looking at the position of the hooves different gaits can be identified. Horses used for riding in the major disciplines in Sweden have three different gaits, walk, trot and canter. Walk is a four-stoke gait, trot is two-stoke and canter is three-stoke. This can be used to classify the different gaits. By looking at the hooves movement in vertical position over time and fitting a sinewave to the data it is possible to see the phase difference in the hooves movement. For walk and trot there was a significant pattern which was easy to identify and corresponded well to the theory of horses movement. For canter our pre-trained model lacked in accuracy, so the output data were insufficient. Therefore it was not possible to find a significant pattern for canter which corresponds to the theory of horses movements. The Fourier Transform were also tested to classify the gaits and when plotted it was possible to detect the different gaits, but not significant enough to be reliable for different horses in different sizes running in different paces. It was also possible to add the data for all four hooves together and fit a sinewave to the added data, and then compare it with the sinewaves for each hoof separately. Depending on the gait the frequency of the sinewaves differed between the hooves separately and added together and the gaits could be identified.