Sensor Networks February 6, 2003 Class Meeting 8 (Images from Prof. Deborah Estrin, USC)
Deborah Estrin Professor, Cornell Tech, New York City ...Lessons from the field of embedded sensing...
Transcript of Deborah Estrin Professor, Cornell Tech, New York City ...Lessons from the field of embedded sensing...
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Motivated by 6 x 109 people on planet earth, their health needs, and economic realities
Embedding mobile computing and research in everyday life
Deborah EstrinProfessor, Cornell Tech, New York City
[email protected] done in collaborators with collaborators from
UCLA, openmhealth.org, iSTC, ...
Enabled by >6 x 109 mobile phone users, increasingly with: GPS, imagers,
touch screens, Internet, app stores
moodphone (TM) Intelubifit UW/Intel welldoc (TM) The Eatery (TM) Tonic-app (TM) openmhealth.org
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from embedded to mobile to participatory sensing
mhealth: ‘personal evidence’, n=me
whats next?personal data APIs, mobile personal informatics, NEW YORK CITY!
an end-to-end argument for driving systems research with authentic applications
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Lessons from the field of embedded sensing 2002-2010
Early themes: many simple measurementssmall platforms
autonomous operation
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Lessons from the field of embedded sensing 2002-2010
Early themes: many simple measurementssmall platforms
autonomous operation
Midterm themes: multi-modal measurements
varied platformshuman-assisted operation
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Lessons from the field of embedded sensing 2002-2010
Early themes: many simple measurementssmall platforms
autonomous operation
Midterm themes: multi-modal measurements
varied platformshuman-assisted operation
Eventual themes: model-based measurement
mobile platformsassistive systems, infovis
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Real time (always on)
Real place (always carried)
Real context (historical, environmental, spatial, social)
Real applications (environment, education, community, health)
Participatory Sensing (starting ~2006)
individuals and communities using personal mobile devices and web services to systematically explore and document their lives
(builds on methodologies of experience sampling [Csik85] and photovoice [Wang95])
w/ Mark Hansen (Statistics/DMA), Jeff Burke (REMAP/TFT)
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Real time (always on)
Real place (always carried)
Real context (historical, environmental, spatial, social)
Real applications (environment, education, community, health)
Participatory Sensing (starting ~2006)
individuals and communities using personal mobile devices and web services to systematically explore and document their lives
(builds on methodologies of experience sampling [Csik85] and photovoice [Wang95])
Chose applications that scaled down... as well as up: i.e., utility at small n so real use can guide iterative cycles of innovation
w/ Mark Hansen (Statistics/DMA), Jeff Burke (REMAP/TFT)
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Assessing environmental factors: Community data gathering (Boyle Heights, 2010)
Acker, Samanta, Belany et al
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6w/ Jeroen Ooms, Mark Hansen, Hongsuda T.
Snackboard dashboard to >3000 entries about snacking collected by high school students in 2012 (J. Ooms)
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Telling traces: PEIR (2007-08)model-based estimation using continuous location-activity-time series
http://peir.cens.ucla.edu
Wired NEXTFEST,
Chicago 2008
w/ Mark Hansen (Statistics/DMA), Jeff Burke (REMAP/TFT); Funded by Nokia
Individual time-location traces used to automatically estimate daily personal carbon impact and air particulate exposure
PEIR Facebook Application
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8UI: E. Wang
Pivot to focus on mobile health (mhealth) ....and personal n=me evidence
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transform previously unmeasured behaviors and practices into personalized, evidence-based, and evidence-producing care
The promise of mobile Health (mHealth)
capture/record activity, mobility, self-reports, tool-
use, “digital exhaust”
symptoms, side-effects, outcome
measures, actions, activities, exposures..
Photo: Marshall Astor, WWW
store, analyze, classify, fuse, mashup, filter, aggregate data
visualize, summarize,
highlight; inform, advise, persuade
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•3 lifestyle behaviors (poor diet, lack of exercise, smoking) cause 1/3rd of US deaths; 50% Americans have 1 or more chronic diseases; age of onset getting younger.
•Over the next 20 years, Non Communicable Diseases will cost worldwide> $ 30 trillion; mental health > $16.1 trillion (WEF, 2011)
•Equip individuals, families w/tools for measurement, management, understanding outside clinical setting
Why chronic disease management?
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5w/ Ida Sim, Open mHealth
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Transformative methodological tool: recasting ‘evidence’
(Complexes of) Exposures
Outcomestrength of association?
population
depressionsertraline individual
Sim, KravitzFriday, April 5, 13
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Transformative methodological tool: recasting ‘evidence’
(Complexes of) Exposures
Outcomestrength of association?
population
depressionsertraline individual
‘does it work on average?’ (RCT)
50
100
sertraline
Depression (PHQ-‐9)
50
venlafaxine
population
Depression (PHQ-‐9)
Sim, KravitzFriday, April 5, 13
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Transformative methodological tool: recasting ‘evidence’
(Complexes of) Exposures
Outcomestrength of association?
population
depressionsertraline individual
‘does it work on average?’ (RCT)
50
100
sertraline
Depression (PHQ-‐9)
50
venlafaxine
population
Depression (PHQ-‐9)
N-of-1 study design: ‘does it work for Mr. Jones?’
PHQ-‐9Effexor
Zoloft
Zoloft
Effexor
Effexor
Zoloft
individual
Mr. JonesPHQ-‐9
1Kravitz, et al. Contemp Clin Trials 2009; 30:436-445 Sim, KravitzFriday, April 5, 13
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Open mHealth co-innovation use case: Diabetes
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End-‐User Dashboards
Correla1ons in 1me and space
Internet
Ramanathan, Selsky, et al
Ac1graphy over space Ac1graphy over 1me
Not just a mobile app: data analysis, sensemaking, as critical and more challenging
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Ramanathan, Selsky, et al
Loca1on and 1me triggered
surveys
Phonetop Bu@ons
Phone-‐based apps
Exercises/toolsInterventionsGamesAssessments
Multiple choiceScale Free textImage capturePersonalization
InternetData collec1on End-‐User
Dashboards
GPS, Wifi, Accel
sms, calls, calendar,
social media
actigraphy,mobility,comm
PassiveMonitoring
Many features apply across applications
Self Report(EMAs)
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“Real Sensor” streams
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http://ginger.io/the-platform/
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18UI: E. Wang
http://ohmage.orgcontinuous activity, location traces and prompted self-report
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Photographic Affect Meter: PAM (Pollak et al)
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My comparisons feedback screen
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Next steps: transforming passive information into behavioral biomarkers
for chronic diseases
Personalized, rapid, indicators of health improvement, relapse, side-effects, symptoms using:
mobility logging and digital traces (vocabulary, games, spending)
Hours at home per day Walking periods > 6 min per day
Today
17.4 h 55 min 0 min 0 min
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Deriving behavioral biomarkers...from app and sensor streams
summarization•ambulatory/sedentary cumulative and durations,walking speed
•sleep times,social interactions•time spent before leaving house, hours spent at home
behavioral biomarker/indicator•in-person variance/patterns of fatigue, pain, depression, insomnia, cognitive function...
state classification•sedentary/ambulatory•at home/work•using apps•communicating
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Hsieh, Tangmunarunkit, Ramanathan, et al
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Behavioral Biomarkers can drive tailored infographics,informational incentives, feedback, game mechanics
ubifit (S. Consolvo et al,
UW/Intel)
had the garden
ubifit participants who…
did NOT have the
garden
Mobile Ambient Wellbeing Display (T. Choudhury, Cornell)
NIH funded new-moms study in progress(Ramanathan, Ketcham, Estrin...)
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Data reduction, selective sharing, privacy
usability and privacy served by sharing only extracted data features
New privacy models emerging for raw data
http://www.weforum.org/issues/rethinking-personal-data
http://personal.com
http://wethedata.org
TMI (too much information) filter summarization
•ambulatory/sedentary cumulative and durations,walking speed
•sleep times,social interactions•time spent before leaving house, hours spent at home
behavioral biomarker/indicator•in-person variance/patterns of fatigue, pain, depression, insomnia, cognitive function...
state classification•sedentary/ambulatory•at home/work•using apps•communicating
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Omics“Big data” (EHRs,
Web mining)
“small data” (mHealth,
digital traces)
Third IT pillar of personalized, precision, medicine
+ +
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8
VS
Open architecture and communityso mHealth solutions can integrate best available apps and techniques
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open architecture for mobile health
a small set of common principles/practices by which these modules are described and interface to one another
activity classification
graphing significant changes in mobility
data storage (e.g., PHR)
Estrin, Sim. Science 2010; 330:759-760Chen, et al. JMIR 2012; 2012;14(4):e112.
proprietary component with omh API
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Open mHealth light-handed approach to semantics
Foster broad ecosystem of software components that can process or visualize a single payload ID
- competition, different algorithms, new approaches ...
- after all ... this is all ‘new stuff’...processes and products will be iterating rapidly as we learn
Enable a data standard process that supports rapid evolution
- fits the desired and unavoidable dynamics of a learning healthcare ecosystem
Data payload defined by Schema ID, version, lightweight schema
- utilize payload IDs to represent existing standards as well
- accommodate both existing software and existing standards
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Whats next ...
mpire: mobile personal informatics research and experimentation
• Open up programmatic access to individuals to obtain their personal digital traces from mobile, search, social, e-commerce, games, apps
• Personal Data APIs to foster personal services/apps
• Testbed in NYC with access to 1000’s of mobile subscribers for experiments with privacy and detailed analytics
mHealth Greenhouse• collaborate with innovative clinicians to develop
new ways to address patient care by leveraging mobile data: behavioral biomarkers
• tools to support patients’ disease-management
• support rapid, iterative prototyping and piloting
THE mHEALTH GREENHOUSE
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End to end arguments in systems-research design: a case for including authentic applications in experimental
systems research
Original argument [Saltzer, Reed, Clark, 1981] • “...functions placed at low levels of a system may be redundant or of little
value when compared with the cost of providing them at that low level.”
• “The argument appeals to application requirements, and provides a rationale for moving function upward in a layered system, closer the application that uses the function.”
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End to end arguments in systems-research design: a case for including authentic applications in experimental
systems research
Original argument [Saltzer, Reed, Clark, 1981] • “...functions placed at low levels of a system may be redundant or of little
value when compared with the cost of providing them at that low level.”
• “The argument appeals to application requirements, and provides a rationale for moving function upward in a layered system, closer the application that uses the function.”
Implications for systems-research/innovation• authentic applications needed as part of systems research exploration to keep
functional and performance requirements on a purposeful track
• need to build...and use...
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End to end arguments in systems-research design: a case for including authentic applications in experimental
systems research
Original argument [Saltzer, Reed, Clark, 1981] • “...functions placed at low levels of a system may be redundant or of little
value when compared with the cost of providing them at that low level.”
• “The argument appeals to application requirements, and provides a rationale for moving function upward in a layered system, closer the application that uses the function.”
Implications for systems-research/innovation• authentic applications needed as part of systems research exploration to keep
functional and performance requirements on a purposeful track
• need to build...and use...
More general lessons from systems • architecture, modularity, well-defined interfaces, analytics are critical
• enable rapid, iterative, automated, learning and sharing across applications, institutions, markets
• importance of shared robust open infrastructure
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Acknowledgments: Collaborators and Sponsors
Collaborators
Technology Collaborators : Deborah Estrin, Tanzeem Choudhury, Mark Hansen, JP Pollak, Nithya Ramanathan, Joshua Selsky, Hongsuda Tangmunarunkit
Application/domain expert faculty/PIs (Health science): Jessica Anker, Jacqueline Casillas, Mary Charlson, Scott Comulada, Julia Hoffman, Patricia Ganz, Cary Reid, Mary Jane Rotheram-Borus, Ida Sim (UCSF), Fred Sabb, Katie Shilton, Dallas Swendeman, Michael Swiernik
Students, Staff: Staff: Betta Dawson, John Jenkins, Cameron Ketcham, Graduate students: Faisal Alquaddoomi, Hossein Falaki, Brent Flagstaff, Andy Hsieh, Jinha Khang, Donnie Kim, Min Mun, Sasank Reddy, Vids Samanta
Sponsors and Partners/Collaborators
Cornell: Cornell Tech, WCMC CHiP
UCLA: CENS, GCFC, CHIPTS, Health Sciences, JCCC
Federal funding: NSF STC and CISE, NIH
Corporate funding: Google, Intel, MSR, Nokia, T-Mobile
Foundations/NGOs: RWJF, CHCF, CRA
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