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