© 2006 MIT Media Lab Social Network Technology to Evaluate and Facilitate Collaboration MIT Media Lab Human Dynamics Group Prof. Alex (Sandy) Pentland Daniel Olguin Olguin Michael Sung NIH Roadmap Interdisciplinary Methodology and Technology Summit North 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 Applications MIThrill 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-time Interest 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 GroupMEDIA Adding Context Awareness to Mobile Devices Modeling User Behavior Classification Accuracy: 80-90% The “Jerk-o-Meter” Speed Dating

© 2006 MIT Media Lab Reality Mining Eigenbehaviors: 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 practices Using 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 SystemShivering Core Temperature Regimes

© 2006 MIT Media Lab MGH Depression and ECT Treatment Study LiveNet Depression Rig Subjective emotion ratings Clinical Outcomes Physiology correlations (1 day) Emotion rating correlations

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