Abstract

Xenopus laevis frogs are a widely used organism to study aspects of modern biology (Harland and Grainger, 2011). Its central nervous system is particularly interesting, because in certain stages of metamorphosis the spinal cord can regenerate after injury and recover swimming. With this in mind, automatic gait analysis could help evaluate the regenerative performance by means of a method that automatically and quantitatively establishes the degree in froglets' limb movement. Here, we present an algorithm that characterizes spinal cord damage in froglets. The proposed method tracks the position of the limbs throughout videos and extracts kinematic features, which posteriorly serve to differentiate froglets with different levels of damage to the spinal cord. The detection algorithm and kinematic features chosen were validated in a pattern recognition experiment in which 90 videos (divided equally in three classes: uninjured, hemisected and transected) were classified. We conclude that our system is effective in the characterization of damage to the spinal cord through video analysis of a swimming froglet with a 97% accuracy. These results potentially validate this methodology to automatically compare the recovery of spinal cord function after different treatments without the need to manually process videos. In addition, the procedure could be used to measure the kinematics and behavioral response of froglets to different experimental conditions such as nutritional state, stress, genetic background and age.