1. Information-centric networking in Healthcare Scenarios Going Beyond Physiological Sensing for Supporting Wellbeing Work by the PAL project with partners being Cambridge University, Essex University, Thales Research, HW Communications and MAC Ltd User evaluation results by Dana Pavel as presented in MindCare 2011 workshop

2. Outline What scenarios do we consider? What information do we consider? – And how to we visualize the information? What are the system-level implications & requirements? Does it really matter? Do users want this? 2

3. Example: Lifestyle management Bob is 55 and he has just found out that he has a high blood pressure and he is at risk for developing a serious heart condition in the future unless he makes some adjustments to his lifestyle. 3

4. Example: Lifestyle management PAL System 4

5. Example: Lifestyle management 5

6. Some of the Challenges Emergency Scenario Produced by CTVC Inc. 6

7. What Information to Consider? And how to visualize it? 7

8. Complex user context Mental context (interest, focus, etc.) Availability context (people or resource) Physical context (position, direction, distance, speed, proximity) Temporal context (absolute, relative, duration) Activity context Emotional context Social context (communication, identity) 8

9. 9

10. Visualizations in self-monitoring systems 10

11. Daily story Visualizations for information collected and derived stored in the personal database Information collected on demand from remote servers Calendar-based interface 11

12. MindCare2011 10:20am. Location: at home. It’s quiet. Using MS Word, writing in a document called MyPaper_v1.doc. Quite active, it seems, based on how many words per minute you typed. Weather today is cloudy/sunny. 12

13. MindCare2011 2:30pm. Location: university. It’s a bit noisy, in a meeting. You are giving a presentation. Getting quite agitated, it seems. Weather now is cloudy/sunny. 13

14. System-Level View Requirements, implications, and approach 14

15. Overview of Requirements 15

16. Key Issues Here Vast and diverse amount of information Security and privacy of utmost importance – Policy-driven, even for informal lifestyle management! Must work anywhere, anytime and on any device – Often said but difficult to achieve! Creating an understanding at user level is important – Amended by contextual evidence, if necessary! 16

17. Our Architecture Vision: Information (From) Everywhere Application Middleware Communication Infrastructure Components Network Awareness Service Awareness Information Policy Framework Governance 17

18. Translated into An Architecture 18 Visualizations/Interactions Applications/Services Middleware Communication Layer Policy Engine DB Resource Discovery Component I/O Component Data gathering & transformation DB Information modelling and correlation (Information Agent) Reasoning Engine Interaction Manager Visualization Manager Rendezvous Point Topology Manager Policy Agent PUB Publisher Subscriber Pub/sub Set_policy Pub/sub Set_policy PURSUIT Pub/sub map Map/unmap/divert Pub/sub I/O Component

19. Our Approach at Communication Level ITF Topology RP Rendezvous Network Network Architecture Service Model Helper Error Ctrl … Fragmentation Caching TM Forwarding Network Forwarding Network Forwarding Network Forwarding Network FN pub sub Apps Node Architecture RP: Rendezvous point ITF: Inter-domain topology formation TM: Topology management FN: Forwarding node Explorative Approach Evolutionary Approach Pub/sub abstraction TCP IP All-Ethernet wired & wireless API 19

20. Interesting Points in an All Information-centric Approach Role of middleware – Pub/sub on routing level already Policies – Integrating information from lower layers Discrimination of data at network layer – Policy-based routing based on, e.g., importance Late binding – Enable anywhere in different ways! 20

21. Gathering Data Applications BT Middleware IP Application Server IP Android OS AIRS BT, BT LEE, RFID,Zigbee IP Mobile Device Motivation  harvest intelligence out there  commoditize acquisition, shift value to intelligence Technical Highlights  Event-based architecture  Transfers only relevant data  Allows for local & remote sensing  Allows for aggregation done in mobile  Utilizes standard Android APIs  Supports more than 50 ‘sensors’, from BT-attached (AliveTech ECG) over system to logical information (such as mood and weather)  Open source  Available on Android Market (search for “airs trossen”) Stationary multisensor module A Embedded Sensors 21

22. Do Users Really Want This? 22

23. User-based evaluations Online survey available at: http://ieg.essex.ac.uk/myror/survey/intro.php http://ieg.essex.ac.uk/myror/survey/intro.php Ongoing user experiments focusing on: 1. What information is perceived as more useful? 2. What correlations are perceived as more useful? 3. How do people want to interact with the system? 4. How do people want to personalize their story? 5. What events are perceived as meaningful by the user? 23

24. Online survey (preliminary) 30 people Questions focus on: – Self-reflective behaviours – Building user-friendly interfaces for such systems Customizing interfaces Sharing Interactions 24

25. Self-reflective behaviours Q1: Do you often think back about what happened during the day? (Often/Not very often/Never) Q2: Do you think about what triggered a certain emotion or behaviour? (Yes/No) Q3: Do you usually propose any change based on self reflection? (Yes/No/Examples) 25

26. Means for self-reflection 26

27. System-related questions Q6: Would you find useful having a system as presented in the scenario? (Yes/No/Examples of useful information) Q7: If you were to be using such a system would you like to be able to see a story generated based on your activity data? (Yes/No/Explain) 27

28. System Demo AIRS & Diary 28

29. Conclusions Lifestyle management is increasingly becoming important – It is an information-driven scenario! Users not only want this understanding – they want to be in control of it! Networking-level aspects impact viability of overall scenarios – Late binding and different abstractions important! 29

30. Acknowledgements to the Larger Team Cambridge: Jat Singh, Jean Beacon Essex: Dana Pavel, Kun Yang, Ken Guild Thales: Adrian Waller, Glyn Jones, Sarah Pennington HW: Souroush Honary, Daniel Essafi, Behzad MAC: Peter Gould, Ying Li Ericsson: Steve Campbell, Mike Wamsley 30

31. Thank you http://www.palproject.org.uk/