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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 Intilles.intille@neu.edu
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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
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Woc ket kit (+ phone)Charge 2
Wear 2 for 24h
Upper + lower
body motion
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pp g p
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Thin for c ontinuous wearability
Actigraph Wocket
Mobile Applic ation Design and Development Summer 2011Production & sizing
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pp g p
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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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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
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