  Human activity encompasses a series of complex spatiotemporal processes that
are difficult to model, but represents an essential component of human exposure
assessment. A significant empirical data source like the American Time Use
Survey (ATUS) can be leveraged to model human activity, but tractable models
require a better stratification of activity data to inform about different, but
classifiable groups of individuals that exhibit similar activities and mobility
patterns. We have developed a simple unsupervised classification and sequence
generation method from existing machine learning algorithms that is capable of
generating coherent and stochastic sequences of activity from the data in the
ATUS. This classification, when combined with any spatiotemporal exposure
profile, allows the development of stochastic models of exposure patterns for
groups of individuals exhibiting similar activity behaviors.
