  Falls are the leading cause of fatal and non-fatal injuries, particularly for
older persons. Imbalance can result from the body's internal causes (illness),
or external causes (active or passive perturbation). Active perturbation
results from applying an external force to a person, while passive perturbation
results from human motion interacting with a static obstacle. This work
proposes a metric that allows for the monitoring of the person's torso and its
correlation to active and passive perturbations. We show that large changes in
the torso sway can be strongly correlated to active perturbations. We also show
that we can reasonably predict the future path and expected change in torso
sway by conditioning the expected path and torso sway on the past trajectory,
torso motion, and the surrounding scene. This could have direct future
applications to fall prevention. Results demonstrate that the torso sway is
strongly correlated with perturbations. And our model is able to make use of
the visual cues presented in the panorama and condition the prediction
accordingly.
