By understanding how routines support people’s everyday activities, we can uncover new subjects for sensing and machine learning. This new data creates new ways for end-user applications to support daily life. I demonstrate the value of this approach using dual-income families.
My studies of family logistics shows that family members sometimes need but do not have access to information about the plans and routines of other family members. Because family members do not document this information, they do not exist as resources family members can turn to when needed.
With only the GPS on commercial mobile phones, we can use machine learning and data mining to automatically document family logistical routines, and present that information to families to help them feel more in control of their lives.