Preprocessing of skeletal keypoints trajectories in the task of laboratory animal behavior recording automation

The automation of action recognition in laboratory animals is a crucial step in simplifying behavioral tests in the fields of pathophysiology and rehabilitation research. The most common method of action recognition is to analyze the trajectories of key skeletal points. However, the existing methods are strongly tied to the specific animal species, selected skeletal points, and set of activities to be recognized. Furthermore, there is a dearth of mathematical formulations of this problem and research on algorithms for filtering obtained trajectories. The research task involves the collection of a dataset for key points detection of Wistar rats and evaluation of algorithms for filtering trajectories from noisy measurements. In considered skeletal model of the rat, a total of thirteen points were selected for the purpose of estimating the behavior along trajectories. A mathematical description of the dynamics of point movement between frames for use in a Kalman filter is provided. Four filtering algorithms are evaluated in terms of accuracy and curve smoothness. The technique of constructing the covariance matrix of the detector noise by analyzing the key point detection errors is developed. The comparison of filtering algorithms shows that the Unscented Kalman filter with nonlinear model and moving average filter yield the most optimal results in this task. The findings of this study allow the use of a mathematical description of system dynamics to estimate the actual trajectory from noisy measurements. Furthermore, the described methodologies are not exclusive to laboratory animals, but can also be applied to human subjects.