big data and the quantified self

Melanie Swan MS Futures Group

+1-650-681-9482@LaBlogga, @DIYgenomics

[email protected]

http://www.youtube.com/TechnologyPhilosophe

October 28, 2013

National Consumer Res Ctr, Helsinki, Finland

Slides: http://slideshare.net/LaBlogga

Big Data and the Quantified Self

October 28, 2013QS Big Data 2

About Melanie Swan Founder DIYgenomics, science and technology

innovator and philosopher Singularity University Instructor, IEET Affiliate

Scholar, EDGE Contributor Education: MBA Finance, Wharton; BA

French/Economics, Georgetown Univ Work experience: Fidelity, JP Morgan, iPass,

RHK/Ovum, Arthur Andersen Sample publications:

Source: http://melanieswan.com/publications.htm

Swan, M. Crowdsourced Health Research Studies: An Important Emerging Complement to Clinical Trials in the Public Health Research Ecosystem. J Med Internet Res 2012, Mar;14(2):e46.

Swan, M. Scaling crowdsourced health studies: the emergence of a new form of contract research organization. Personalized Medicine 2012, Mar;9(2):223-234.

Swan, M. Steady advance of stem cell therapies. Rejuvenation Res 2011, Dec;14(6):699-704. Swan, M., Hathaway, K., Hogg, C., McCauley, R., Vollrath, A. Citizen science genomics as a model for crowdsourced

preventive medicine research. J Participat Med 2010, Dec 23; 2:e20. Swan, M. Multigenic Condition Risk Assessment in Direct-to-Consumer Genomic Services. Genet Med 2010,

May;12(5):279-88. Swan, M. Emerging patient-driven health care models: an examination of health social networks, consumer personalized

medicine and quantified self-tracking. Int J Environ Res Public Health 2009, 2, 492-525.

October 28, 2013QS Big Data

Conceptualizing Big Data Categories

3

Personal Data

Group Data

Tension: Individual vs Institution

Sense of data belonging to a group


Agenda

Personal Data Quantified Self Quantified Self and Big Data Advanced QS Concepts

Group Data Urban Data

Conclusion

4


What is the Quantified Self?

5

Individual engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information

Data acquisition through technology: wearable sensors, mobile apps, software interfaces, and online communities

Proactive stance: obtain and act on information

Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.


Smartring (ElectricFoxy), Electronic tattoos (mc10), $1 blood API (Sano Intelligence), Continuous Monitors (Medtronic)

6

Smartphone, Fitbit, Smartwatch (Pebble), Electronic T-shirt (Carre)

QS Sensor Mania! Wearable Electronics

Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012.


Wearable Personal Information Ecosystem

7

Smart Gadgetry Creates Continuous Personal Information Climate

PC/Tablet/Cloud

SmartphoneNew Wearable Categories: Smartwatch and AR/Glass

AR = Augmented Reality


Next-gen Mini: BioSensor Electronic Tattoos

8Source: http://www.jacobsschool.ucsd.edu/pulse/winter2013/page3.shtml#tattoos

Electrochemical Sensors

Tactile Intelligence:Haptic Data Glove

Chemical SensorsDisposable Electronics

Wearable Electronics: Detect External BioChemical Threats and Track Internal Vital Signs


Quantified Self Worldwide Community

Goal: personalized knowledge through quantified self-tracking

‘Show n tell’ meetups What did you do? How did you do it? What

did you learn?

9Source: Swan, M. Overview of Crowdsourced Health Research Studies. 2012.

Videos, Conferences, Meetup Groups


Source: http://www.meetup.com/Quantified-Self-Biohacking-Finland/


Quantified Self Project Examples

Low-cost home-administered blood, urine, saliva tests

OrSense continuous non-invasive glucose monitoring

Cholestech LDX home cholesterol test

ZRT Labs dried blood spot tests

Food consumption (1 yr)1 and the Butter Mind study2

Study

1Source: http://flowingdata.com/2011/06/29/a-year-of-food-consumption-visualized2Source: http://quantifiedself.com/2011/01/results-of-the-buttermind-experiment


Quantified Self Measurements…

121METs = Metabolic equivalents Source: http://measuredme.com/2012/10/building-that-perfect-quantified-self-app-notes-to-developers-and-qs-community-html/

Physical Activities Miles, steps, calories, repetitions, sets, METs1

Diet and Nutrition Calories consumed, carbs, fat, protein, specific ingredients, glycemic index,

satiety, portions, supplement doses, tastiness, cost, location

Psychological, Mental, and Cognitive States and Traits Mood, happiness, irritation, emotion, anxiety, esteem, depression, confidence IQ, alertness, focus, selective/sustained/divided attention, reaction, memory,

verbal fluency, patience, creativity, reasoning, psychomotor vigilance

Environmental Variables Location, architecture, weather, noise, pollution, clutter, light, season

Situational Variables Context, situation, gratification of situation, time of day, day of week

Social Variables Influence, trust, charisma, karma, current role/status in the group or social network


The Quantified Self is Mainstream

13

Self-tracking statistics 60% US adults track weight, diet, or exercise 33% US adults monitor blood sugar, blood pressure,

headaches, or sleep patterns 9% receive text message health alerts 40,000 smartphone health applications

QS thought leadership Press : BBC, Forbes, and Vanity Fair Electronics show focus at CES 2013 Health 2.0: “500+ companies making

self-management tools; VC funding up 20%”



Hype Curves per Google Trends

14

2011 2013 2011 2013


QS Experimentation Motivation and Features

15Source: DIYgenomics Knowledge Generation through Self-Experimentation Study http://genomera.com/studies/knowledge-generation-through-self-experimentation

DIYgenomics QS Study (n=37)

Desired outcome: optimality and improvement (vs pathology resolution) Personalized intervention for depression,

low energy, sleep quality, productivity, and cognitive alertness

Rapid experimental iteration through solutions and kinds of solutions

Resolution point found within weeks Pragmatic problem-solving focus, little

introspection


History of the Quantified Self

16

Sanctorius of Padua 16th c: energy expenditure in living systems; 30 years of QS weight/food data

QS Philosophers Epicureans, Heidegger, Foucault): ‘care

of the self’ ‘Self’: recent concept of modernity

QS: contemporary formalization using measurement, science, and technology to bring order and control to the natural world, including the human body



Sensor Mania!

17Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012.


Wireless Internet-of-Things (IOT)


Image credit: Cisco


6 bn Current IOT devices to double by 2016

19Source: http://www.businessinsider.com/growth-in-the-internet-of-things-2013-10?IR=T


IOT World of Smart Matter

IOT Definition: digital networks of physical objects linked by the Internet that interact through web services

Usual gadgetry (e.g.; smartphones, tablets) and now everyday objects: cars, food, clothing, appliances, materials, parts, buildings, roads

Embedded microprocessors in 5% human-constructed objects (2012)1

201Source: Vinge, V. Who’s Afraid of First Movers? The Singularity Summit 2012. http://singularitysummit.com/schedule


IOT Contributing to Explosion of Big Data

Big Data: data sets too large and complex to process with on-hand database management tools

Examples Walmart : 1 million transactions/hr

transmitted to 3 PB database BBC: 7 PB video served/month from

100 PB physical disk space

Structured and unstructured data (not pre-defined)

21Source: http://en.wikipedia.org/wiki/Big_data, http://wikibon.org/blog/big-data-statistics


Annual data creation on the order of zetabytes 90% of the world’s data created in the last 2 years Fastest growing segment: human biology-related data

Defining Trend of Current Era: Big Data

Source: Mary Meeker, Internet Trends, http://www.kpcb.com/insights/2013-internet-trendshttp://www.intel.com/content/dam/www/public/us/en/documents/white-papers/healthcare-leveraging-big-data-paper.pdf

2 year doubling cycle


QS is inherently a Big Data problem

23

Data collection, processing, analysis Cloud computing for consumer processing

Local computing tools are not available to store, query, and manipulate QS data sets

Cloud-based analysis: Predictive modeling, natural-language processing, machine learning algorithms over very-large data sets of heterogeneous data

Rapid growth in QS data sets Manually-tracked ‘small data’ is now

automatically-collected ‘big data’ Examples: heart rate monitor data - 250

samples/second (9 GB/person/month); personal health ‘omics’ files



QS Big Data: Personal Health ‘Omics’

24

DNA: SNP mutations

Microbiomics

Proteomics

RNA expression profiling

Epigenetics

Health 2.0:Personal Health

InformaticsDNA: Structural

variation

Metabolomics

Source: Academic papers re: integrated health data streams: Auffray C, et al. Looking back at genomic medicine in 2011. Genome Med. 2012 Jan 30;4(1):9. Chen R et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012 Mar 16;148(6):1293-307.


Big Data: Integrated QS Data Streams

25Swan, M. Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory

Biocitizen. J Pers Med 2012, 2(3), 93-118.

Genome SNP mutations

Structural variationEpigenetics

Microbiome

Transcriptome

Environmentome

Metabolome

Diseasome

Proteome

Personal and Family Health History

Prescription History

Lab Tests: History and Current

Demographic Data

Self-reported data: health, exercise,

food, mood journals, etc.

Biosensor Data Objective Metrics

Quantified Self Device Data

Mobile App Data

Quantified Self Data Streams

Traditional Data StreamsOmics Data Streams

Standardized Instrument Response

Legend: Consumer-available


APIs and Multi-QS Data Stream Integration

26


Fluxstream Unified QS Dashboard

27Source: http://johnfass.wordpress.com/2012/09/06/bodytrackfluxtream/


Sen.se Integrated QS Dashboard

28Source: http://blog.sen.se/post/19174708614/mashups-turning-your-data-into-something-useable-and

‘Mulitviz’ display: investigate correlation between coffee consumption, social interaction, and mood


Wholly different concept and relation to data

Formerly everything signal, now 99% noise Medium of big data opens up new methods: Exception, characterization, variability, pattern recognition,

correlation, prediction, early warnings

Allows attitudinal shift to active from reactive Two-way communication: translate biometric variability in the

personal informatics climate to real-time recommendations Example: degradation in sleep quality and hemoglobin A1C

levels predict diabetes onset by 10 years1

291Source: Heianza et al. High normal HbA(1c) levels were associated with impaired insulin secretion. Diabet Med 2012. 29:1285-1290.


Big Data opens up new Methods

Google: large corpora and simple algorithms Foundational characterization (previously unavailable)

Longitudinal baseline measures of internal and external daily rhythms, normal deviation patterns, contingency adjustments, anomaly, and emergent phenomena

New kinds of Pattern Recognition (different structures) Analyze data in multiple paradigms: time, frequency, episode, cycle,

and systemic variables New trends, cyclicality, episodic triggers, and other elements that

are not clear in traditional time-linear data

Multi-disciplinarity Turbulence, topology, chaos, complexity, etc. models

30Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.


Opportunity: QS Data Commons

Common repository for personal informatics data streams Fitbit, Jawbone UP, Nike, Withings, myZeo,

23andMe, Glass, Pebble, Basis, BodyMedia

Architecting consumer-friendly models Open-access databases, developer APIs, front-

end web services and mobile apps (Precedent: public genotype/phenotype data)

Accommodate multi-tier privacy standards Ecosystem value propositions: service providers,

research community, biometric data-owners Role of public and private service providers



Github: de facto QS Data Commons

32Source: https://github.com/beaugunderson/genome


QS Frontier: Mental Performance Optimization

33

‘Siri 2.0’ Personal Virtual Coach from DIYgenomics

Sources: http://cbits.northwestern.edu and http://quantifiedself.com/2009/03/a-few-weeks-ago-i

Source: DIYgenomics Social Intelligence Studyhttp://diygenomics.pbworks.com/w/page/48946791/social_intelligence

PTSD App Mood Management Apps from

Mobilyze and M. Morris

Source: http://www.ptsd.va.gov/public/pages/ptsdcoach.asp


Next-gen QS Services: Quality of Life

34

QS Aspiration Apps: Happiness, Emotive State (personal and group), Well-being, Goal Achievement

Category and Name Website URLHappiness Tracking Track Your Happiness http://www.trackyourhappiness.org/Mappiness http://www.mappiness.org.uk/The H(app)athon Project http://www.happathon.com/MoodPanda http://moodpanda.com/TechurSelf http://www.techurself.com/urwellEmotion Tracking and SharingGotta Feeling http://gottafeeling.com/Emotish http://emotish.com/Feelytics http://feelytics.me/Expereal http://expereal.com/Population-level Emotion BarometersWe Feel Fine http://wefeelfine.org/moodmap http://themoodmap.co.uk/Pulse of the Nation http://www.ccs.neu.edu/home/amislove/twittermood/Twitter Mood Map

http://www.newscientist.com/blogs/onepercent/2011/09/twitter-reveals-the-worlds-emo-1.html

Wisdom 2.0 http://wisdom2summit.com/Personal Wellbeing PlatformsGravityEight http://www.gravityeight.com/MindBloom https://www.mindbloom.com/Get Some Headspace http://www.getsomeheadspace.com/Curious http://wearecurio.us/uGooder http://www.ugooder.com/Goal Achievement PlatformsuMotif http://www.uMotif.com/DidThis http://blog.didthis.com/Schemer https://www.schemer.com/ (personalized recommendations)Pledge/Incentive-Based Goal Achievement PlatformsGymPact http://www.gym-pact.com/Stick http://www.stickk.com/ Beeminder https://www.beeminder.com/



Next-gen QS Services: Behavior Change

35Source: http://askmeevery.com/


Next-gen QS Services: Behavior Change

Shikake: Sensors embedded in physical objects to trigger a physical or psychological behavior change

Examples: Transparent trash cans Trash cans playing an

appreciative sound to encourage litter to be deposited

Stairs light up on approach Appreciative ping/noise from

QS gadgetry

36Source: http://mtmr.jp/en/papers/taai2013v2.pdf


Next-gen QS Services: 3D Quantification

37

BodyMetrics and Poikos: Fitness and Clothing Customization Apps

OMsignal: Smart Apparel 24/7 Biometric Monitoring


Continuous Information Climate

Fourth-person perspective: Immersed in infinite data flow, we shed bits of information to the data flow, the data flow responds by sending information to us



Magnetic Sense: Finger and Arm Magnets

North Paw Haptic Compass Anklet and Heart Sparkhttp://www.youtube.com/watch?v=D4shfNufqSg

http://sensebridge.net/projects/heart-spark

Extending our senses in new ways to perceive data as sensation

Serendipitous Joy: Smile-triggered EMG muscle sensor with an LED headband display

Building Exosenses for the Qualified Self



Exosenses as Quantified Intermediates

Networked quantified intermediates for human senses: smarter, visible, sharable through big data processing

Vague sense of heart rate variability, blood pressure; haptically-available exosenses make the data explicit

Haptics, audio, visual, taste, olfactory mechanisms to make metrics explicit: heart rate variability, blood pressure, galvanic skin response, stress level

Skill as exosense: technology as memory, self-experimentation as a form of exosense

40

Gut-on-a-chip

Lung-on-a-chip

Source: web.mit.edu/newsoffice/2012/human-body-on-a-chip-research-funding-0724.html

Nose-on-a-chip


Neural Tracking: QS Big Data Frontier

24/7 Consumer EEG, Eye-tracking, Emotion-Mapping, Augmented Reality Glasses

41

Consumer EEG Rigs

1.0

2.0

Augmented Reality Glasses



QS Big Data: Biocitizen Volition

Individual

2. Peer collaboration and health advisors

Health social networks, crowdsourced studies, health advisors, wellness coaches, preventive care plans,

boutique physicians, genetics coaches, aestheticians, medical tourism

3. Public health systemDeep expertise of traditional health system

for disease and trauma treatment

1. Continuous health information climate Automated digital health monitoring, self-tracking devices, and mobile apps providing personalized recommendations

Source: Extended from Swan, M. Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int. J. Environ. Res. Public Health 2009, 2, 492-525.


Conceptualizing Big Data Categories

43

Personal Data

Group Data


Agenda



Conclusion

44


Group Data: Smart City, Future City

Image: http://www.sydmead.com


Global Population: Growing and Aging

46Source: UN Habitat – 2010

http://avondaleassetmanagement.blogspot.com/2012/05/japan-aging-population.html


3 billion new Internet users by 2020

47Source: Peter Diamandis Singularity University


Over 50% worldwide population in 2008 5 billion in 2030 (estimated) Megacity: (>10 million and possibly 2,000/km2)

Human Urbanization: Living in Cities

48


Megacity Growth Rates

Source: Wikipedia


Big Urban Data: Killer Apps

50Source: MIT Senseable City Lab

Adaptive lighting, smart waste, pest control, hygiene management, eTolls, public transportation, traffic management, smart grid, asset tracking, parking

Flexible services responding in real-time to individual and community-level demand


Data Signature of Humanity

51Source: http://senseable.mit.edu/signature-of-humanity/

MIT SENSEable City Lab – the Real-Time City


3D Buildings + Population Density

52Source: ViziCities


3D Tweet Landscape

53Source: http://vimeo.com/67872925http://www.slideshare.net/robhawkes/bringing-cities-to-life-using-big-data-webgl


3D Urban Data Viz: Decision-making Tool

54Source: http://www.wired.com/autopia/2013/08/london-underground-3d-map/


Group Data: Office Building Community

55Source: http://www.siembieda.com/burg.html, BURG, San Jose CA 2010


Himalayas Water Tower

Big Data 3D Printed Dwellings of the Future

Living Treehouses – Mitchell Joachim

Masdar, Abu Dhabi – Energy City of the Future


Urban Agriculture: Vertical Farms

57

San Diego, California (planned)

Singapore (existing)


Reconfiguration of Space: Seasteading


Transportation Revolution

59

Solar Power: Tesla + Solar City

Self-Driving CarPersonalized Pod Transport

Source: Google's Self-Driving Cars Complete 300K Miles Without Accident, Deemed Ready for Commutinghttp://techcrunch.com/2012/08/07/google-cars-300000-miles-without-accident/


Crowdsourcing

60Source: Eric Whitacre's Virtual Choir 3, 'Water Night' (2012), http://www.youtube.com/watch?v=V3rRaL-Czxw


Pervasiveness of Crowd Models

Crowdsourcing: coordination of large numbers of individuals (the crowd) through an open call on the Internet in the conduct of some sort of activity Economics: crowdsourced labor marketplaces, crowdfunding,

grouppurchasing, data competition (Kaggle) Politics: flashmobs, organizing, opinion-shifting, data-mining Social: blogs, social networks, meetup, online dating Art & Entertainment: virtual reality, multiplayer games Education: MOOCs (massively open online courses) Health: health social networks, digital health experimentation

communities, quantified self Digital public goods: Wikipedia, online health databanks, data

commons resources, crowdscience competitions

61


Genomera – Crowdsourced Study Platform

62Source: http://genomera.com/studies/dopamine-genes-and-rapid-reality-adaptation-in-thinking


Agenda



Conclusion

63


But wait…Limitations and Risks

Transition to access not ownership models Data rights and responsibilities

Personal data and group data

Regulatory and policy tensions Surveillance (top-down) vs souveillance (bottom-up) Multi-tier privacy and sharing preferences Digital divide accessibility, non-discrimination

Precedent = Uninformed Consumer: Lack of access conferred (e.g.; health data, genomics, credit scoring)

Consumer non-adoption, ease-of-use, social acceptance, meaningful value propositions

64


Proliferation of New QS Big Data Flows

QS Device Data Biometric data (HRM), personal genomic data Personal medical and health data QS neural-tracking eye-tracking affect data

Personal IOT Data Cell phone, wearable electronics data Smartphone digital identity & payment

Personal Urban Data Smart home, smart car Smart city data (e.g.; transportation)

Personal Robotics Data

65


Top 10 QS Big Data Trends

Internet-of-Things (IOT)Sensor Networks

3 billion New People Online

3D Information Visualization

Megacity Growth

Smart CityFuture City

QS Device Ecosystem

Crowdsourcing

Self-Empowerment DIY Attitude

66

Wearable Electronics

Urban Data

Biocitizen

Personal Data Group Data


Heidegger and Big Data

Technology is not good or bad in itself, technology is an enabler, not a means to an end (Kant: end not means)

Our attunement to the background of technology as a capacity for revealing the world could help us away from our lostness in daily projects to see the possibilities for the true meaningfulness of our being

Source: Heidegger, M. The Question Concerning Technology, 1954


QS Big Data Summary

Next-gen QS services Wearable Electronics as the QS platform Improve quality of life, facilitate behavior change

IOT continuous personal information climates QS Big Data

Wholly different relation to data: 99% noise Rights and responsibilities model of data access

Group Data Megacity growth, urban data flow, 3 bn coming online

Personal Data Technology-enabled biocitizen-consumer takes action

68

Melanie Swan MS Futures Group

+1-650-681-9482@LaBlogga, @DIYgenomics

[email protected]

http://www.youtube.com/TechnologyPhilosophe

October 28, 2013

National Consumer Res Ctr, Helsinki, Finland

Slides: http://slideshare.net/LaBlogga

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Big Data and the Quantified Self

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