using gps to learn family routines: a summary

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. In this talk, I demonstrate how, using only mobile phone GPS, we can learn a model of dual-income family logistical routines, and present that information to families to help them feel more in control of their lives.Dual-income families rely on routines to support the detail required to make and monitor transportation plans for kids’ activities. Successful routines reduce anxiety levels, and provide parents with the feeling of confidence, competence, and control. My study 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 these routines, they do not exist as a resource family members can turn to when needed.I demonstrate how machine learning and data mining can automatically document those undocumented family transportation routines, generating new resources that family members can turn to when needed. I demonstrate that family members find this new resource useful to their coordination, and that it helps them feel like more competent parents, more in control of their lives.BIOScott Davidoff has a PhD in Human-Computer Interaction from Carnegie Mellon, where he was advised by Anind Dey and John Zimmerman. Scott also worked at Microsoft Research Cambridge (UK) with Shahram Izadi and Alex Taylor. Previously, he spent 7 years managing interaction design boutique Scott Davidoff Design, where he developed new products for companies like AOL, SBC Ameritech, and TV Guide. Scott also has an MS in Computer Science (Research) and an M.HCI in Human-Computer Interaction (Practice), both from Carnegie Mellon.For more information: http://scottdavidoff.com