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Modeling and Understanding Human Routine Behavior

Nikola Banovic 1 Tofi Buzali 1 Fanny Chevalier 2 Jennifer Mankoff 1 Anind Dey 1
2 MJOLNIR - Computing tools to empower users
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
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.
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https://hal.inria.fr/hal-01416119
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Nikola Banovic, Tofi Buzali, Fanny Chevalier, Jennifer Mankoff, Anind Dey. Modeling and Understanding Human Routine Behavior. ACM CHI Conference on Human Factors in Computing Systems 2016, ACM, May 2016, Santa Clara, California, United States. pp.248 - 260, ⟨10.1145/2858036.2858557⟩. ⟨hal-01416119⟩

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