© 2006 MIT Media Lab

Social Network Technology to Evaluate and Facilitate

Collaboration

MIT Media LabHuman Dynamics GroupProf. Alex (Sandy) Pentland

Daniel Olguin OlguinMichael Sung

NIH Roadmap Interdisciplinary Methodology and Technology SummitNorth Bethesda, MD August 21-22, 2006

© 2006 MIT Media Lab

Human Dynamics Research

GroupMEDIA

Learning Humans

LiveNet

Reality Mining

Sensible Organizations

© 2006 MIT Media Lab

Underlying Framework Social signals

– From speech: engagement, emphasis, mirroring, activity

– From body gesture: motion, energy, activity

We have been able to identify:– Central connectors, boundary

spanners, information brokers and peripheral people in a social network

– The boss in an organization– The leader of a team– The outcome of negotiations– The degree of persuasiveness in

speech– Group affiliations

Automatically captured group dynamics

© 2006 MIT Media Lab

Wearable Computing

Electronic Badges

Body Sensor Networks

Human Activity Recognition

Healthcare ApplicationsMIThrill

Body motion

Face-to face interactions

© 2006 MIT Media Lab

Social Motion

Conferences and Career Fairs

Identifying team leaders and experts

Affiliation and Social Relationship Inference

Automatic Real-timeInterest Measurement

© 2006 MIT Media Lab

LifeWear

Human Activity Recognition Using Wearable Sensors– PDA– Camera– Microphones– Accelerometers

Automatic Multimedia Collection of Interesting Moments

© 2006 MIT Media Lab

Healthcare Applications

DiaBetNet– Wearable computer

for diabetic children Wearable Monitor for

Parkinson Disease Treatment

LiveNet DiaBetNet: Interactive game to monitor blood

glucose levels and make predictions

© 2006 MIT Media Lab

GroupMEDIAAdding Context Awareness to Mobile

Devices

Modeling User Behavior

Classification Accuracy: 80-

90%The “Jerk-o-Meter”

Speed Dating

© 2006 MIT Media Lab

Reality MiningEigenbehaviors: Identifying structure in routine

Proximity Sensing:Bluetooth + Cell tower ID

Social Serendipity

© 2006 MIT Media Lab

Sensible Organizations

Understanding Organizational

Dynamics

Efficiency

Creativity

Productivity

Innovation

Capturing everyday social signals in real

organizations to improve managerial practicesUsing social sensors

technology to measure:

Combining social,

physical, and digital

information

© 2006 MIT Media Lab

Social Sensors Technology Extended mobile phones:

– Bluetooth-enabled smart-phones– Wearable electronic badges with social sensors

Real-time speech feature analysis Context awareness, user localization and proximity

sensing Activity recognition Push to talk system with voice-controlled interface

Mobile phones are socially accepted wearable computers

© 2006 MIT Media Lab

Wearable Communicator Badge

© 2006 MIT Media Lab

Technological Challenges

User acceptance– Small and comfortable to wear

Hardware design, development, and support– Prototyping, manufacturing, and

deployment Real time data collection and

processing Large-scale user studies

© 2006 MIT Media Lab

Methodological Challenges Relate social measurements to productivity,

efficiency, creativity and innovation– Develop new metrics to achieve quantitative

measurements– Evaluate qualitative data: consumer satisfaction

Perform dynamic social network analysis Capture individual and group dynamics in

locally and geographically distributed teams New management methodologies based on

social sensors

© 2006 MIT Media Lab

The LiveNet System Distributed modular framework Commodity PDA/cell phone hardware Variety of custom/commercial sensors Real-time data streaming Resource allocation/discovery Local processing for context

classification Rapid application prototyping

LiveNet: a flexible mobile platform that is at the same time a long-term health monitor, context-aware agent, multi-modal feedback interface for proactive healthcare applications

© 2006 MIT Media Lab

Non-invasive Sensing Movement

– spectral features, energy, orientation Voice Features

– energy, pitch, entropy, voicing dynamics

Temperature/heat flux– Metabolic activity, environmental

cues Heart rate

– IBI, HRV measures, spectral ratios Skin conductance

– slope analysis, peak detection Behavioral

– Location, sleep/activity patterns, socialization dynamics

BioSense Board

Bluetooth Location Beacon

© 2006 MIT Media Lab

MIT PokerMetrics Stress Study

LiveNet PokerMetrics Setup Real-time Physiology (Stressful vs Non-Stressful)

© 2006 MIT Media Lab

U.S. Army Soldier Physiology Monitoring

LiveNet ARIEM System Shivering Core Temperature Regimes

© 2006 MIT Media Lab

MGH Depression and ECT Treatment Study

LiveNet Depression Rig

Subjective emotion ratingsClinical Outcomes

Physiology correlations (1 day)Emotion rating correlations

© 2006 MIT Media Lab

ThanksFor more information visit:

http://hd.media.mit.eduor e-mail us at:

dolguin@media.mit.edu