using gps to learn family routines: a summary
DESCRIPTION
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.comTRANSCRIPT
Using GPS to Learn Family Routines: a Summary
Scott Davidoff
Everyday routines are unavailable to most sensing systems
Smart phones can make routines computable
Why does that matter?
Use routine models to lower anxiety when juggling kids’ rides
Identify mechanisms that result in coordination problems for families
CONTRIBUTION 1
Demonstrate a new activity lies in the scope of sensing + machine learning
CONTRIBUTION 2
Visualize the day’s plan using learned activity, place, pick-up time and driver
CONTRIBUTION 3
Contribution 1
Routines are not documented on calendars or elsewhere
1
Families do not recall one another’s routines perfectly
2
Families make plans that depend on incorrect information
3
Why is this a problem?
Sometimes kids get left at activities. Anxiety for everybody.
Contribution 2
Sense pick-ups and drop-offs1
PLACE 1
t4
PLACE 2
t3t2t1
Sensing Drop-Offs
Parent Child
PLACE 1
t4
PLACE 2
t3t2t1
Sensing Pick-Ups
Parent Child
Over 90% precision and over 90% recall without supervision
Predict drivers2
Use a decision tree:a) distribution over
past driversb) real-time locationc) observables
Unsupervised, online learning using 1 week of training data is 72% accurate
Unsupervised, online learning using 4 week of training data is 88% accurate
Predict Late Pick-Ups3
25
PredictionClass
Observable
LearnedModel
0.6590.8010.825
tideal – 30tideal – 10tideal
Time to Pick-Up
A’ Value
Contribution 3
The Family Time-Flow ® shows the plan for the day using routine models
