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How Routine Learners can Support Family Coordination

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(1)

How Routine Learners can Support Family Coordination

Carnegie Mellon Human-Computer Interaction Institute

by Gianluca Vinzens

Scott Davidoff, John Zimmerman + Anind K. Dey

(2)

Overview

How Routine Learners can Support Family Coordination

Learning Patterns of Pick-ups and Drop-offs to Support Busy Family Coordination

Unremarkable Computing

(3)

How Routine Learners can Support Family

Coordination

(4)

Intention

Discussion of conceptual feasibility

Roadmap

1. Analyze what families would find

valuable

(5)

Data Collection (1)

6 dual-income families

6 months

(6)

Data Collection (2)

Quantitative

Six month of field observation

Four families completed

528 unique interview sessions

2112 person days

(7)

Data Collection (3)

Qualitative

Evaluation of knowledge of others routines (Activity interviews)

Identification of routine or non-routine

(8)

Contributions (1)

Routines and family life

40 %

(9)

Contributions (2)

Routine knowledge of others is incomplete or inaccurate

(10)

Contributions (3)

Calendars hold deviations not routine

90 %

(11)

Contributions (4)

Small information gaps lead to stressful situations

(12)

Future Potential

Access to routine

Augmented calendars

Augmented reminders

Use of more sensors

Better routine detection algorithms

(13)

Reviews (1)

Rating: 2 (accept)

Positive

Extensive data collection

Base for applications supporting family coordination

Interesting to read with many examples

(14)

Reviews (2)

Negative

No technical aspects

Only GPS location

Children and mobile phones

(15)

Learning Patterns of Pick-ups and Drop-offs to Support

Busy Family Coordination

(16)

Setup

Dual-income families

GPS location data (once per minute)

Data from first paper

(17)

Intention

Pick-ups and drop-offs

Detect pick-ups and drop-offs

Predict driver

Infer if child will be forgotten

(18)

Recognizing Rides (1)

States

People

(19)

Recognizing Rides (2)

Pick-up

Drop-off

(20)

Recognizing Rides (3)

Precision

90.1 %

Recall

95.5 %

(21)

Predicting Drivers (1)

Feature Vector

Labeling and weighting

(22)

Predicting Drivers (2)

Accuracy

Sliding window

1 week: 72.1 %

4 weeks:

87.7 %

(23)

Forgetting Children (1)

10 minutes late

Features

(24)

Forgetting Children (2)

Bayesian Network

(25)

Forgetting Children (3)

ROC (Receiver Operating Characteristic)

(26)

Optimizations

Increase GPS rates

Other modes of transport

other than one parent, one child, one car

Better driver prediction model

“only “ 70 - 85 %

(27)

Future Potential

Awareness Systems

Calendars

Reminder Systems

(28)

Unremarkable

Computing

(29)

Intention

Analyze home / domestic life routines

Make technology “invisible in use”

(30)

Scenarios

Door as a means of communication

Knocking, opening, context dependent

Alarm clock becomes routine

Failure would be noted

Routines are unknown to yourself

(31)

Conclusions (1)

Invisibility in use

perceptual invisibility

(32)

Conclusions (2)

Augment the action not

artifacts per se

(33)

Conclusions (3)

Support the doing without

description of activities

(34)

Thanks for your attention

(35)

Questions / Discussion

Use of more sensors?

Potential of routine detection algorithms?

T-Patterns

Eigenbehaviors

Topic Models

Data collection and children?

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