Class1 Northeastern Mob App Class

7/29/2019 Class1 Northeastern Mob App Class

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Mobile Applic ation Design and Development Summer 2011

Northeastern University 1

Mobile Application (Design and)Development

Prof. Stephen [email protected]


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Welcome!

Goa ls of this c ourse:

Help you lea rn about mob ile appdevelop ment and best prac tic es

Help you lea rn about mob ile interac tiondesign and rap id p rototyping stra teg ies

Provide you with the tools, knowledge, andexc use to c rea te a novel mobile app tha thelps solve a serious p rob lem tha t streng thensyour p rogramming portfolio


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Who am I?

BSE in CSE from Penn

Ph.D. from MIT (c omputer vision)

Home of the Future and a rc hitec ts

Hea lth and House_n Northeastern! (Sep 2011)

New Ph.D. Persona l Hea lth Informatics)

Interests: mob ile hea lth, games for hea lth,mob ile and home sensing and pa tternrec ognition, UI design, AI


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Mobile Applications

What a re they?

Any app lic a tion tha t runs on a mob iledevice

Types (and evo lution)

Web apps: run in a web b rowserHTML, Java Sc ript, Flash, server-side c omponents, etc .

Native: compiled binaries for the deviceNot c ross-plat form, but more interesting options

This c lass: Android phones


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Native development environments Op tions

Java ME .NET Compac t Framework (C++, C# , VB.NET) forWindows Mob ile

Qua lcomms BREW (C or C++)

Symbian (C++) Blac kBerry (Java) Android (Java) iPhone (Ob jec tive-C)

Is having so many choic es and so muc hindustry turmoil/ c ompetition a good thing?


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Development Environments

Most p la tforms have a n SDK that you can

download and build aga inst

Every p la tform has an emula tor that you

c an use to test your apps Most emula tors a re c onfigurab le to match

a variety of mob ile devic es

Various sc reen sizes, memory limita tions,tab lets, etc .

In p rac tice, emulators quite limited


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xCode IDE & iPhone Emulator


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Ec lipse and Android Emulator


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Why a spec ia l course?

There is a growing demand for experts

Mobile c omputing is transformative

It s easier to learn with a group

Mob ile app interac tion design offers

unique c hallenges and opportunities


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Mobile devices the good

Always with the user

Inc reasingly powerful devic es

Typ ic a lly GPS c apab le

Typ ic a lly have ac c elerometer Designed for c ommunic a tion

2+ types of wireless c onnec tions Many apps a re free or low-c ost


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Mobiles the not-so-good

Limited p roc essor speed

Limited ba ttery life

Limited and slow network ac c ess

Limited sc reen size Limited or awkward input

(none grea t: soft keyboa rd , phone keyp ad , touc h sc reen, stylus, speec h)

Inc onsistent p la tforms ac ross devic es Warning: Blac kberry thumb


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Primary design challenges

Short bursty interac tions

User expec ta tions for simp lic ity

Interruptions!

Limited input moda lity Da ta reliab ility (and multip le p oints of desired data ac c ess)

Stand ing out in a c rowd Aggressive opera ting systems

In the future: sec urity/ p rivac y


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How will the c ourse work? 7 week, intensive, immersive Android

p rogramming experienc e. Are you ready?

Foc us on both DESIGN and DEVELOPMENT,w ith some user testing thrown in.

Syllabus (in p rogress) online:http:/ / ww w.cc s.neu.ed u/ home/ intille/ tea ching/ MobileAp p lic a tionDevelop ment2011Syllabus.htm


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Project Sudoku

Bogg le

Something tha t helps peop le with hea lthbehavior c hange that is add ic tive,

innova tive, and useful and uses thephones unique c apab ilities ... tha t isdesigned by you and turns hea ds


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Projec t contest Everyone in the c lass will try/ c ritique apps

from other tea ms

At end of the c ourse we will vote for theTop App , a long with a sma ll group ofinvited expert judges

Small p rize awarded and b ragg ing rights


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More on projec t options Plug-in for the CITY p rojec t

Innovative use of motion sensor for

enc ourag ing physic a l ac tivity or lesssedentary behavior

(Advanced : c ould use Woc kets)


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Demo: CITY App http://www.dukecitystudy.org


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Lets start thinking c reatively... Timing of delivery of ta ilored information

important for behavior c hange

Mob ile d evic es with rea l-time feedbac k

c rea te novel (and enga ging?) op tions tha t can t be a c hieved without the tec hnology

There a re some design and tec hnic a lc hallenges, but they can be overc omewith more researc h


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Opportunity

Big opportunities a t the intersec tion usingrea l-time, c ost-effec tive feedbac k ... Some

killer apps yet to be built.

Human

ComputerInteraction

Sensors &

MachineLearning

BehavioralScience

Leverage:Consumer elect ronics

Mobile t echnologies

Focus:Body

HomeWorkplaceCommunity

Personal}


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Engineering getting c loser... Phone sensors

Ac c elerometer

GPS Others...

Wearab le sensors Woc kets

(accelerometers) Heart ra te monito rs

Self-report on phone

Detect change in activity;Motivate behavior changes;

Info at teachable moment

healthapplications

machinelearningalgorithms

Physic a l ac tivity

Ac tivity type

Intensity Duration

Loca tion

Other behaviors Physiolog ic a l sta te

Self-rep orted sta te


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Inspiration Suppose a person visits the doc tor and the

doc tor rec ommends more physic a lactivity

Sc enario 1: Doc tor g ives a lec ture with suggestions

Hands out some read ing materia l

Sc enario 2: Person hires a (good) persona l tra iner


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Inspiration Person wants to inc rease b risk wa lking

Sc enario 1:

Doc tor p rovides a pedometer At end of a day, 1500 more steps to goa l

Sc enario 2: Doc tor p rovides just-in-time phone app

When wa lking, no tifies 1500 more steps to goa l

Timing of information delivery is powerful

& c ould d ramatic a lly improve intervention


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New opportunity Context-sensitive just-in-time c ontent

delivery a t p oints of dec ision, behavior, orconsequence [Fogg 99]

Exp loit the sensor-enab led mob ile d evic esand persona l c omputers peop le a re

ac quiring for enterta inment andc ommunic a tion ... and their sensing andproc essing power


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Strategy

Simp le messages(points of dec ision/ beha vior/ c onseq uenc e)

Right t ime

Right p lac e

Ta ilored , non-d isrup tive

Repea ted and c onsistent

Impac t on behavior c hange?

Requires computational sensing

Requires portable computing

Requires attentionto interface design

Requires patience(computers excellent at this)


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Does just- in-time work?


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Example 1: StepLively Based on operant cond itioning

Uses positive, just-in-time reinforc ement

Multi-level (day, week) goa l setting

aga inst own behavior tha t is c ontinuouslyand automatic a lly measured using phonemotion sensor

Provides p leasant aud io reinforc ers basedon c urrent behavior


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Standard phone app(Windows Mob ile 6.x version)


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Simple startup


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Tutoria l


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Tutoria l

M bil A li i D i d D l S 2011


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Starting motion(grad student a t 3AM!)

M bil A li ti D i d D l t S 2011


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Moving ... eventually get pointNo movement a t

3AM from priortime so getahead and

receiverreinforcement(sound + sta r)

Goa l: say ahea d

of the past



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Fill in gaps via self report



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Navigate to see goal



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Day goal and week goal (then reset)



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Customize reinforc ement



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Example 2: Woc kets Goa l

24/ 7 measurement of physica l ac tivity of Type

Intensity

Dura tion Locat ion

For months+

Keep c ost low so suitab le for c ohort stud ies Exp loit consumer phone tec hnolog ies

All op en sourc e



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Vision: population-scale

24/ 7 Real-t ime PA detect ionand cont ext -sensit ive self

repor t w it h sensors (GPS, phone)

Data sentt o server f oranalysis & remot eadministration Years

I deas tomakeflexible &

personallyvaluable& engaging

Part icipant has flexibilit yin how t o wear/ use

sensors

App Store

New surveys/ interventions

remot ely loaded &administered; remot e soft w areupdates w / new capabil i t ies

Real-t ime f eedback toencourage compliance



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ob e pp c at o es g a d e e op e t Su e 0


Woc ket kit (+ phone)Charge 2

Wear 2 for 24h

Upper + lower

body motion



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pp g p


Thin for c ontinuous wearability

Actigraph Wocket

Mobile Applic ation Design and Development Summer 2011Production & sizing


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pp g p


Production&sizing

WRISTANDANKLEBANDDESIGN

POCKETBAGDESIGN

Usability c ritic al



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Ac



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Continuous data c ollec tion 2 sensors wearing , 2 c ha rg ing

Summary da ta sent hourly to server

Plug p hone nightly (up loads raw da ta)

Woc kets last 42+ hours, phone waking day Phone d etec ts da ta qua lity & missingness

in rea l time and p rovides feedbac k to

enc ourage study c ompliance



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Robustness

Note : Ac tivities manua lly labeled ...

Working on rea l-time detec tion of some ac tivity types andc ontext (posture, ambula tion, struc ture exerc ise, etc .)



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Robustness



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Robustness



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Robustness



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Status Both projec ts went through itera tive

design; about to be dep loyed inva lida tion stud ies

Pilot testing began with researc h tea msand friend lies

Identified many c ha llenges an in b oth c asesmade ma jor design c hanges

Next: 7 of the c ha llenges and imp lic a tions

Documents

Class1 Northeastern Mob App Class