  Fall is a leading cause of death which suffers the elderly and society. Timed
Up and Go (TUG) test is a common tool for fall risk assessment. In this paper,
we propose a method for predicting TUG score from gait characteristics
extracted from video with computer vision and machine learning technologies.
First, 3D pose is estimated from video captured with 2D and 3D cameras during
human motion and then a group of gait characteristics are computed from 3D pose
series. After that, copula entropy is used to select those characteristics
which are mostly associated with TUG score. Finally, the selected
characteristics are fed into the predictive models to predict TUG score.
Experiments on real world data demonstrated the effectiveness of the proposed
method. As a byproduct, the associations between TUG score and several gait
characteristics are discovered, which laid the scientific foundation of the
proposed method and make the predictive models such built interpretable to
clinical users.
