HCC: Small: Learning Routines to Support People's Activities

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

People construct routines as they repeatedly perform the same sequence of actions. Routines provide a huge benefit by freeing people?s attention, allowing them to carry out their daily tasks without constantly thinking about every little thing they must do. Problems begin to arise when people must deviate from their routines. Families rely heavily on their routines to address the complex logistics and conflicting agendas of work, school, family, and enrichment activities. However, families often deviate from their routines, and when breakdowns in the plans occur, they feel their lives are out of control. This research will develop a system that learns the routine movements of family members, and a planning system that leverages this model in order to generate a speculative plan for future days. The system will also predict conflicts with scheduled deviations and detect when plans begin to breakdown, such as when someone forgets to deviate from a routine. A calendar interface that displays the routine movements of family members along with their scheduled deviations and a small set of reminder applications that help people enact their plans and that support them when plans breakdown will form the basis for evaluating the underlying systems. This research is transformative in the novel integration of machine learning and planning techniques, and its application to a real-world and complex problem. Finally, this research provides insights on how intelligent, ubiquitous computing technology influences families? feelings of control and their quality of life. The proposed work has the potential to significantly improve the quality of life for millions of families by reducing stress caused from breakdowns in plans and routines. Lowering stress can improve the quality of marriages, the quality of parenting, and the physical and mental health of children. We will involve undergraduate and graduate students in our research and will incorporate our findings into our courses on ubiquitous computing, interaction design, and on smart homes. We expect that our focus on a social problem will attract non-science-focused students to science and expose science-focused students to design methods of inquiry.