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Computing for Human Experience

Keynote at On-the-Move Federated Conference, October 2011: http://www.onthemove-conferences.org/

Amit Sheth

Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled ComputingWright State University, Dayton, OH, USA

Special thanks & contributions: Cory Henson

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with 7 billion people

Our 1 world

living 1 experience at a time

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with a stream of experiences to be …

shared

rememberedcultiv

ated

realized enjo

yed

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Today, technology increasingly engages individuals, society and humanity with …

5 billionmobile phones

40+ billion mobile sensors

1-2 billioncomputer

s

http://www.gartner.com/it/page.jsp?id=703807

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With constant connectivity enabled through global networks

5 billionmobile phones

40+ billion mobile sensors

1-2 billioncomputer

s

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So, how can we leverage this tech to improve our experiences

without losing ourselves in the process?

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Or drown in data?

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From machine-centric to human-centric design

Machines to accommodate our experiences,as opposed to the other way around

Computing to liberate

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To accomplish this requires a fundamental shift in how we interact and communicate with computational machines

We must take a more holistic view of computation,as a shared universe, populated by people and machines working in harmony to achieve our highest aspirations

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We have caught glimpses of this vision …

Man-Machine Symbiosis – T. O’Reilly

Humanist Computing – J. Rossiter

Memex – V. Bush

Ambient Intelligence – E. Zelkha, B. Epstein

Experiential Computing – R. Jain

Ubiquitous Computing – M. Weiser

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Let this affect be positive

The ways in which technology and humansinteract are fundamentally changing

In turn, our (human) experiences are changing

Our activities, decisions, thoughts, and feelingsare affected by the ubiquitous integration of technology into the fabric of our lives

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Computing for Human Experience

Amit Sheth

Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled ComputingWright State University, Dayton, OH, USA

Special thanks & contributions: Cory Henson

Amit Sheth

Ashutosh Jadhav

Hemant Purohit

Vinh Nguyen

Michael Cooney

Lu ChenPavan

KapanipathiPramod

Anantharam

Sujan Perera

Alan Smith

Pramod Koneru

Maryam Panahiazar

Sarasi Lalithsena Prateek Jain

Matthan Sink

Cory Henson

Ajith Ranabahu

Kalpa Gunaratna

Delroy Cameron

Sanjaya Wijeratne

Wenbo Wang

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Human capabilities such as sensing, perception, attention, memory, decision making, control, etc.

CHE is an approach to improving the human condition through computational means, and with minimal burden

This may be achieved through the contextual assistance, augmentation, and absolution of human capabilities

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Consider the following example …

A cross-country flight from New York to Los Angeles on a Boeing 737 plane generates a massive 240 terabytes of data

- GigaOmni Media

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But, how much data is generated regarding the health and well-being of the pilot or passengers?

zero, none, zilch!

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Image the ability to monitor and control our health with the same care and precision that goes into the 737.

And not just providing doctors with such control, but you and me.

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Health information is now available from multiple sources

• medical records• background knowledge • social networks• personal observations • sensors• etc.

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Sensors, actuators, and mobile computing are playing an increasingly important role in providing data for early phases of the health-care life-cycle

This represents a fundamental shift: • people are now empowered to monitor and manage their own health; • and doctors are given access to more data about their patients

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Unfortunately, when personal health data is collected and presented, it often looks like this … gibberish.

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Health Metrics with Meaning

Personal Health Dashboard

What is needed is a more intuitive and intelligent representation of our health.

Image: http://bit.ly/lV2V73

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How is this accomplished?

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• Integration of heterogeneous, multimodal data• Bridging the physical-cyber-social divide• Elevating abstractions that machines and people understand• Semantics at an extraordinary Scale

These enablers are brought together through Semantic Web technologies

Key Enablers of CHE

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Foundation on which these enablers stand

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Foundation on which these enablers stand

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Integration of heterogeneous, multimodal data

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Background Knowledge: ontologies, knowledge bases, LOD, databases, etc.

Social/Community Data: social network data, wisdom of the crowds, etc.

Sensor Data: observations from machine sensors, citizen sensors (i.e., patients, doctors), laboratory experiments, etc.

Personal Context: location, schedule, items (e.g., accessible sensors), etc.

Personal Medical History: Electronic Medical Records, Personal Health Records, Patient Visit Records, etc.

Integration of heterogeneous, multimodal data

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Communications using online technologies to share opinions, insights, experiences and perspectives with each other.

What is Social Media?

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Blogs – DiabetesMine, HealthMatters, WebMD, NYT HealthBlog, etc.

Microblogs – Livestrong, Stupid Cancer, etc.

Social Networks – OrganizedWisdom, PatientsLikeMe, DailyStrength, NursesRecommendDoctors, CureTogether, etc.

Podcasts – John Hopkins Medical Podcasts, Mayo Clinic, etc.

Forums – Revolution Health Groups, Google Health Groups, etc.

Popular types of Healthcare Social Media

31http://www.slideshare.net/IQLab/social-media-101-for-pharma-3494462

60% of physicians either use or are interested in using social networks

32http://www.slideshare.net/IQLab/social-media-101-for-pharma-3494462

People are turning to each other online to understand their health

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83% of online adults search for health information

http://www.slideshare.net/IQLab/social-media-101-for-pharma-3494462

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83% of online adults search for health information

60% of them look for the experience of “someone like me”

http://www.slideshare.net/IQLab/social-media-101-for-pharma-3494462

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"I don't know, but I can try to find out" is the default setting for people with health questions.

Savannah Fox, The Social Life of Health Information, Pew Internet Report, May 12, 2011. Available at http://www.pewinternet.org/Reports/2011/Social-Life-of-Health-Info.aspx

"I know, and I want to share my knowledge" is the leading edge of health care.

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Intra Community Activity and connectivity– How well connected are individual nodes (People)– What keeps them strongly connected over time

(Relationship types - Knowledge of Content)

Inter-Community Connectivity• Any bridges to connect to the other community? (People)• Any Similarity in actions with the other community (Can

Content help?)Image: http://themelis-cuiper.com

Will the two communities coordinate well during an event- crisis or disaster?• Interplay between all three dimensions

– P, C, N

People-Content-Network Analysis

For more info: http://www.slideshare.net/knoesis/understanding-usercommunity-engagement-by-multifaceted-features-a-case-study-on-twitter

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People-Content-Network Analysis

External Knowledge bases

Dynamic Domain Model for the event

Event oriented Community

Social Network

Mined User Interests and User Types

User Profiles

SEMANTIC ASSOCIATION TO UNDERSTAND ENHANCED ENGAGEMENT LEVEL

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Bridging the physical-cyber-social divide

Computation is no longer confined to pure symbol manipulation. The previously strict relations between the digital and physical world are blurring.

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Psyleron’s Mind-Lamp (Princeton U), connections between the mind and the physical world.

Neuro Sky's mind-controlled headset to play a video game.

MIT’s Fluid Interface Group: wearable device with a projector for deep interactions with the environment

Bridging the physical-cyber divide

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Foursquare is an online application which integrates a persons physical location and social network.

Bridging the physical-cyber-social divide

Community of enthusiasts that share experiences of self-tracking and measurement.

FitBit Community allows the automated collection and sharing of health-related data, goals, and achievements

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Tweeting Sensorssensors are becoming social

Bridging the physical-cyber-social divide

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Bridging the physical-cyber-social divide

Select topicSelect date

Topic tree

Spatial Marker

N-gram summaries

Wikipedia articles

Reference news

Related tweets

Images & Videos

Tweet trafficSentiment AnalysisNetwork

Analysis

Community Interaction of Various user

types

For more info: http://twitris.knoesis.org/

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Heliopolis is a suburb of

Cairo.

Dynamic Model Creation

Continuous Semantics

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Even

ts

“Both Ahmadinejad & Mousavi declare victory in Iranian Elections.”

“situation in tehran University is so worrisome. police have attacked to girls dormitory #tehran #iranelection”

“Reports from Azadi Square - 4 people killed by police, people killed police who shot. More shots being fired #iranelections”June 12 2009 June 13 2009 June 15 2009

Key p

hra

ses

Mod

els

Ahmadinejad & Mousavi are politicians in

Iran

Tehran University is a University in

Iran

Azadi Square is a city square in

Tehran

Dynamic Model Creation

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The design and building of physical-cyber-social systems requires effective conceptualization and communication between people and machines.

To reach this vision requires advancement in the area of machine perception, enabling machines the ability to abstract over low-level observations.

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Abstraction

Abstraction provides the ability to interpret and synthesize information in a way that affords effective understanding and communication of ideas, feelings, perceptions, etc. between machines and people.

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The process of interpreting stimuli is called perception; and studying this extraordinary human capability can lead to insights for developing effective machine perception.

People are excellent at abstraction; of sensing and interpreting stimuli to understand and interact with the world.

“real-world”

conceptualization of “real-world”

Sensor

Sensor Data / Social Data

observation

perception

Physical

Cyber

Social

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Both people and machines are capable of observing qualities, such as redness.

* Formally described in a sensor/observation ontology

observesObserver Quality

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Sensor and Sensor Network (SSN) Ontology

http://www.w3.org/2005/Incubator/ssn/XGR-ssn/

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The ability to perceive is afforded through the use of background knowledge, relating observable qualities to entities in the world.

* Formally described in domain ontologies

(and knowledge bases)

inheres in

Quality

Entity

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http://linkedsensordata.com

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With the help of sophisticated inference, both people and machines are also capable of perceiving entities, such as apples.

• the ability to degrade gracefully with incomplete information

• the ability to minimize explanations based on new information

• the ability to reason over data on the Web

• fast (tractable)

perceivesEntityPerceiver

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minimizeexplanations

degrade gracefully

tractable

Web reasoning

Abductive Logichigh complexity

Deductive Logic (e.g., OWL)(relatively) low complexity

Perceptual Inference(i.e., abstraction)

Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, Pascal Hitzler. Representation of Parsimonious Covering Theory in OWL-DL. Proceedings of the 8th International Workshop on OWL: Experiences and Directions (OWLED 2011), San Francisco, CA, USA, June 5-6, 2011.

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• Goal is to account for observed symptoms with plausible explanatory hypotheses (abductive logic)

• Driven by background knowledge modeled as a bipartite graph causally linking disorders to manifestations

Yun Peng, James A. Reggia, "Abductive Inference Models for Diagnostic Problem-Solving"

m1

m2

m3

d1

d2

d3m4

disorder manifestationcauses

explanationobservations

Parsimonious Covering Theory

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PCT Parsimonious Cover

• coverage: an explanation is a cover if, for each observation, there is a causal relation from a disorder contained in the explanation to the observation

• parsimony: an explanation is parsimonious, or best, if it matches some criteria of suitability (i.e., single disorder assumption)

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Given

PCT problem P is a 4-tuple D, M, C, Γ⟨ ⟩

• D is a finite set of disorders• M is a finite set of manifestations• C is the causation function [C : D Powerset(M)]⟶• Γ is the set of observations [Γ M ]⊆

Δ is a valid explanation (i.e., is a parsimonious cover)

Goal

Translate P into OWL, o(P), such that o(P) ⊧ Δ

Convert PCT to OWL

Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, Pascal Hitzler. Representation of Parsimonious Covering Theory in OWL-DL. Proceedings of the 8th International Workshop on OWL: Experiences and Directions (OWLED 2011), San Francisco, CA, USA, June 5-6, 2011.

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headache

extreme exhaustion

severe ache and pain

stuffy nose

sneezing

sore throat

severe cough

mild ache and pain

mild cough

flu

cold

fever

disorder manifestationcauses

PCT Background

Knowledge in OWL

disorders (D)

for all d ∈ D, write d rdf:type Disorderex: flu rdf:type Disorder

cold rdf:type Disorder

manifestations (M)

for all m ∈ M, write m rdf:type Manifestation

ex: fever rdf:type Manifestationheadache rdf:type Manifestation …

causes relations (C)

for all (d, m) ∈ C, write d causes mex: flu causes fever

flu causes headache …

Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, Pascal Hitzler. Representation of Parsimonious Covering Theory in OWL-DL. Proceedings of the 8th International Workshop on OWL: Experiences and Directions (OWLED 2011), San Francisco, CA, USA, June 5-6, 2011.

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observations (Γ)

for mi ∈ Γ, i =1 … n, write

Explanation owl:equivalentClasscauses value m1 and … causes value mn

ex: Explanation owl:equivalentClasscauses value sneezing andcauses value sore-throat causes value mild-cough

explanation (Δ)

Δ rdf:type Explanation, is deduced

ex: cold rdf:type Explanationflu rdf:type Explanation

and

PCT Observations and

Explanations in OWL

Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, Pascal Hitzler. Representation of Parsimonious Covering Theory in OWL-DL. Proceedings of the 8th International Workshop on OWL: Experiences and Directions (OWLED 2011), San Francisco, CA, USA, June 5-6, 2011.

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The ability to perceive efficiently is afforded through the cyclical exchange of information between observers and perceivers.

Traditionally called the Perception Cycle

(or Active Perception)

sendsfocus

sends observation

Observer

Perceiver

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Nessier’s Perception Cycle

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Cognitive Theories of Perception (timeline)

1970’s – Perception is an active, cyclical process of exploration and interpretation. - Nessier’s Perception Cycle

1980’s – The perception cycle is driven by background knowledge in order to generate and test hypotheses. - Richard Gregory (optical illusions)

1990’s – In order to effectively test hypotheses, some observations are more informative than others. - Norwich’s Entropy Theory of Perception

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Key Insights

• Background knowledge plays a crucial role in perception; what we know (or think we know/believe) influences our perception of the world.

• Semantics will allow us to realize computational models of perception based on background knowledge.

• Internet/Web expands our background knowledge to a global scope; thus our perception is global in scope

• Social networks influence our knowledge and beliefs, thus influencing our perception

Contemporary Issues

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observes

inheres in

Integrated together, we have an general model – capable of abstraction – relating observers, perceivers, and background knowledge.

perceives

sendsfocus

sends observation

Observer Quality

EntityPerceiver

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Modeled in set-theoretic notation with components mapped to Parsimonious Covering Theory and OWL

Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology, 2011 (accepted).

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Applications of

HealthcareWeather Rescue

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Weather Application

Detection of events, such as blizzards, from weather station observations on LinkedSensorData

Weather Application

Demos: Real-Time Feature Streams

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Weather ApplicationSECURE: Semantics Empowered Rescue Environment

Rescue robots detect different types of fires, which may require different methods/tools to extinguish, and relays this knowledge to first responders.

Demo: SECURE: Semantics Empowered Rescue Environment

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Weather ApplicationHealthcare Application

Detection of errors in Electronic Medical Records and missing knowledge in a cardiology domain model

EMR

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Weather ApplicationHealthcare Application

EMR: "Her prognosis is poor both short term and long term, however, we will do everything possible to keep her alive and battle this infection."

SNM:40733004_infection SNM:68566005_infection_urinary_tract

A syntax based NLP extractor (such as Medlee) can extract this term and annotate as SNM:40733004_infection

By utilizing IntellegO and cardiology background knowledge, we can more accurately annotate the term as SNM:68566005_infection_urinary_tract

without IntellegO

with IntellegO

Problem Problem

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Weather ApplicationHealthcare Application

EMR: ”The patient is to receive 2 fluid buloses."

SNM:32457005_body_fluid

A syntax based NLP extractor (such as Medlee) can extract this term and annotate as SNM:32457005_body_fluid

without IntellegO

Problem

Fluid is part of buloses treatment, not a problem

with IntellegO

By utilizing IntellegO and cardiology background knowledge, we can determine that this is an incorrect annotation.

Treatment

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In 2008, the rate of data generation surpassed storage capacity. With 7 billion people, and a growing number of sensors, how can such a such a system scale? By shining a light on relevant human experience, supported by knowledge, while dimming the minutia of data.

Semantic Scalability

http://gigaom.com/cloud/sensor-networks-top-social-networks-for-big-data-2

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Semantic Scalability

ex. – keyword based search/index, non-textual data, multimodal data for an event

It is clear that purely syntax-based solutions will not scale

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Semantic Scalability

1. Focusing attention on important information and ignoring irrelevant data

2. Converting low-level data (observations) to high-level knowledge (abstractions)

3. Utilizing CHE technology to more evenly distribute responsibility and activities among people and machines

Path to Web scale semantics

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We were able to demonstrate 50% savings in sensing resource requirements during the detection of a blizzard.

1. Focusing attention on important information and ignoring irrelevant data

Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology, 2012. (accepted)

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2. Converting low-level data to high-level knowledge (observations to abstractions)

Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology, 2012. (accepted)

Experiment – during a blizzard, we utilized Intelleg0 to collect and analyze over 110,000 sensor observations, from:

• 800 weather stations (~5 sensors per station)• across 5 states (Utah, Nevada, Colorado, Wyoming, and

Idaho) • for 6 days (April 1 – 6, 2003)

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2. Converting low-level data to high-level knowledge (observations to abstractions)

We were able to demonstrate an order of magnitude resource savings between storing observations vs. relevant abstractions

Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology, 2012. (accepted)

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2. Converting low-level data to high-level knowledge (observations to abstractions)

Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology, 2012. (accepted)

While this is a good result, the benefit provided for a single person – a single experience – is far more dramatic.

There are almost 7 billion people on earth, and only ~10-15 million doctors (~700:1 - 467:1)

3. Utilizing CHE technology to more evenly distribute responsibility and activities among people and machines

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Providing people with the tools to monitor and manage their own health will dramatically reduce the burden on doctors, and improve the health of the people

These doctors are severely overburdened, answering less than 60% of questions posed by patients regarding their health and well being

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The health-care ecosystem of the future includes

machines, people, and social networks continuously, ubiquitously, and unobtrusively

monitoring and managing our health

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CHE approach to Health Care

1 2 3

Continuous Monitoring Personal Assessment Medical Service

Auxiliary Information – background knowledge, social/community support, personal context, personal medical history

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Continuous Monitoring Phase

Monitoring health metrics and vital signs utilizing unobtrusive body sensors

Continuously collecting information, watching for worrisome symptoms

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Personal Assessment Phase

Assessment of symptoms from personal observation and/or health sensorsavailable at home.

Utilizing background knowledge, personal medical history, and current sensor data to formulate and ask specific questions of patient that will aid in explaining symptoms.

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Medical Services Phase

Assessment of symptoms gathered from continuous and personal phases, with additional sophisticated equipment, advanced treatment, and specialized medical knowledge not previously available.

Utilizing background knowledge, personal medical history, and current sensor data to formulate a diagnosis.

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Continuous Personal Medical

Personal Medical History(e.g., Electronic Medical Record,genomic sequence)

Background Knowledge(e.g., Ontologies, Knowledgebases)

Auxiliary Information

Symptoms/Explanations

Symptoms/Explanations

access & update (PHR)

access &update (PHR)

access & update (EMR)

CHE approach to Health Care

Personal Context(e.g., available sensors,location, schedule)

Social/Community Support(e.g., Patient Network, crowd sourcing)

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Continuous Monitoring Phase: Example

• Abnormal heart rate• Clammy skin

• Panic Disorder• Hypoglycemia• Hyperthyroidism• Heart Attack• Septic Shock

• Check phone for instructions• Patient has history of Heart Disease

Observed Symptoms Possible Explanations

Electronic Medical Record

Health Alert

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Basis is a wrist-watch that also monitors pulse rate, movement, temperature, and galvanic skin response.

Continuous Phase Technology

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Fitbit Tracker uses a MEMS 3-axis accelerometer that measures your motion patterns to tell you your calories burned, steps taken, distance traveled, and sleep quality.

Continuous Phase Technology

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Personal Assessment Phase: Example

Are you feeling lightheaded?

Are you have trouble taking deep breaths?

yes

yes

1. Take medication: Methimazole2. See doctor: how about Tues. @ 11am?

• Patient has history of Hyperthyroidism• Patient has prescription for Methimazole

Have you taken your Methimazole medication?

Do you have low blood pressure when standing?

yes

• Abnormal heart rate• Clammy skin• Lightheaded• Trouble breathing• Low blood pressure

• Panic Disorder• Hypoglycemia• Hyperthyroidism• Heart Attack• Septic Shock

Observed Symptoms Possible Explanations

Electronic Medical Record

Health Alert

no

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Lark is a sleep sensor that monitors circadian rhythms and functions as an "un-alarm," vibrating to wake you at a point in your sleep cycle when you feel alert, not groggy.

Personal Phase Technology

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Instant Heart Rate takes your pulse when you place your finger over your phone’s camera lens. The app uses light from the camera flash to detect color changes caused by blood moving through your finger.

Personal Phase Technology

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Telcare makes a blood glucose meter (right) for diabetics that broadcasts readings to a mobile-phone app (center) where patients can see results and set goals.

Personal Phase Technology

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iBGStar is a plug-in glucose meter for the iPhone, developed by Sanofi-Aventis, providing the ability for patients to monitor and manage Diabetes.

Personal Phase Technology

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Withings Blood Pressure Monitor provides easy and convenient blood pressure readings in the convenience of home.

Personal Phase Technology

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WebMD provides a wealth of health information and an application to diagnose symptoms.

Personal Phase Technology

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Medical Services Phase: Example

• Patient has history of Hyperthyroidism• Patient has prescription for Methimazole

Are your blood sugar levels low?

• Abnormal heart rate• Clammy skin• Lightheaded• Trouble breathing• Low blood pressure

• Hypoglycemia• Hyperthyroidism

Observed Symptoms Possible Explanations

Electronic Medical Record

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Doctor.

Medical Phase Technology

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Health Guard provides a secure way to store and analyze health records for casual browsing or emergency use (i.e., MS Health Vault records).

Medical Phase Technology

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Mobile MIM gives physicians a sophisticated, hands-on mobile system for viewing and annotating radiology images, such as CT scans.

Medical Phase Technology

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Dr. Watson is a health and medical question and answering system developed by IBM, utilizing supercomputer intelligence for medical diagnostics.

Medical Phase Technology

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Continuous Personal Medical

Personal Medical History(e.g., Electronic Medical Record,genomic sequence)

Background Knowledge(e.g., Ontologies, Knowledgebases)

Auxiliary Information

Symptoms/Explanations

Symptoms/Explanations

access & update (PHR)

access &update (PHR)

access & update (EMR)

CHE approach to Health Care

Personal Context(e.g., available sensors,location, schedule)

Social/Community Support(e.g., Patient Network, crowd sourcing)

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Improving the experience of health-care improves all other experiences

CHE holds the potential to revolutionize the practice of health-care by embracing the relationship between ourselves, our machines, and our health

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“The most profound technologies are those that

disappear. They weave themselves into the fabric of

everyday life until they are indistinguishable from

it.” – M. Weiser

Physical-Cyber-Social

Abstraction

Integration Scalability

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thank you, and please visit us at

http://knoesis.org

Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled ComputingWright State University, Dayton, Ohio, USA

More: Vision Paper: Computing for Human Experience:http://wiki.knoesis.org/index.php/Computing_For_Human_Experience

Computing for Human Experience

Image credits:

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