Spatiotemporal Parsing of Motor Kinematics for Assessing Stroke Recovery

Stroke is a major cause of adult disability and rehabilitative training is the prevailing approach to enhance motor recovery. However, the way rehabilitation helps to restore lost motor functions by continuous reshaping of kinematics is still an open research question. We follow the established setup of a rat model before/after stroke in the motor cortex to analyze the subtle changes in hand motor function solely based on video. Since nuances of paw articulation are crucial, mere tracking and trajectory analysis is insufficient. Thus, we propose an automatic spatiotemporal parsing of grasping kinematics based on a max-projection of randomized exemplar classifiers. A large ensemble of these discriminative predictors of hand posture is automatically learned and yields a measure of grasping similarity. This non-parametric distributed representation effectively captures the nuances of hand posture and its deformation over time. A max-margin projection then not only quantifies functional deficiencies, but also back-projects them accurately to specific defects in the grasping sequence to provide neuroscience with a better understanding of the precise effects of rehabilitation. Moreover, evaluation shows that our fully automatic approach is reliable and more efficient than the prevalent manual analysis of the day.