Modeling and Understanding Human Routine Behavior

Jennifer Mankoff

Abstract:

Human routines are blueprints of behavior, which allow
people to accomplish purposeful repetitive tasks at many
levels, ranging from the structure of their day to how they
drive through an intersection. People express their routines
through actions that they perform in the particular situations
that triggered those actions. An ability to model routines
and understand the situations in which they are likely to
occur could allow technology to help people improve their
bad habits, inexpert behavior, and other suboptimal
routines. However, existing routine models do not capture
the causal relationships between situations and actions that
describe routines. Our main contribution is the insight that
byproducts of an existing activity prediction algorithm can
be used to model those causal relationships in routines. We
apply this algorithm on two example datasets, and show
that the modeled routines are meaningful—that they are
predictive of people’s actions and that the modeled causal
relationships provide insights about the routines that match
findings from previous research. Our approach offers a
generalizable solution to model and reason about routines.