Project:
Spike Train Driven Dynamical Models for Human Actions
We investigate dynamical models of human motion that can
support both synthesis and analysis tasks. We seek models that have fine-scale
representational power and can therefore model subtle
differences in the way an action is performed.
To this end, we model an observed action as an (unknown) linear
time-invariant dynamical model of relatively small order,
driven by a sparse bounded input signal.
Our motivating intuition is that the time-invariant
dynamics will capture the unchanging physical characteristics
of an actor, while the inputs used to excite the system
will correspond to a causal signature of the action
being performed.
The Model
Given a time series data (e.g. motion capture data):
