Spike Train Driven Dynamical Models for Human Actions

Introduction

Reconstruction Example

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):

- Estimate the state of the model
- Identify the model parameters
- Estimate the input that drives the system