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The Assisted Cognition Project

The Assisted Cognition Project. Henry Kautz, Dieter Fox, Gaetano Boriello Lin Liao, Brian Ferris, Evan Welborne (UW CSE) Don Patterson (UW / UC Irvine) Kurt Johnson, Pat Brown, Mark Harniss (UW Rehabilitation Medicine) Matthai Philipose (Intel Research Seattle).

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The Assisted Cognition Project

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  1. The Assisted Cognition Project Henry Kautz, Dieter Fox, Gaetano BorielloLin Liao, Brian Ferris, Evan Welborne(UW CSE) Don Patterson(UW / UC Irvine) Kurt Johnson, Pat Brown, Mark Harniss(UW Rehabilitation Medicine) Matthai Philipose(Intel Research Seattle)

  2. Trend 1: Sensing Infrastructure • Robust direct-sensing technology • GPS-enabled phones • RFID tagged products • Wearable multi-modal sensors • Rapid commercial deployment

  3. Trend 2: Healthcare Crisis • Demand for community integration of the cognitively disabled • 100,000 @ year disabled by traumatic brain injury • 7.5 million in US with mental retardation • 4 million in US with Alzheimer’s • Family burnout • Nationwide shortage of professionals

  4. Assisted Cognition • Technology to support independent living by people with cognitive disabilities • at home • at work • throughout the community by • Understanding human behavior from sensor data • Actively prompting and advising • Alerting human caregivers when necessary

  5. Building Partnerships • UW Assisted Cognition seminar • CSE, medicine, nursing, Intel • ACCESS • UW CSE & Rehabilitation Medicine • Grant from NIDDR (Dept. of Education) • Help cognitively disabled use public transportation • Prototype: Opportunity Knocks • Intel Proactive Health effort • Computing for wellness & caregiving • Promote partnerships with government, universities, healthcare organizations • Intel Seattle: sensors for activity tracking

  6. Example • Way-finding Assistant • Help user travel throughout community • On foot • Using public transportation • Detect user errors • Proactively help user recover • “You missed your stop, so get off at the next stop and then wait for the #16 bus...” • Potential users • TBI, MR, mild memory impairment

  7. Example • ADL Assistant • Activities of daily living • Eating, bathing, dressing, ... • Cooking, cleaning, emailing, ... • Monitoring • Changes in ADLs signal changes in health • Reminding / prompting • “Time to take your blue meds” • Step-by-step guidance • “Turn on the tap ... now pick up the brush ...” • Potential users • Disabled, ordinary aging

  8. General Model user model geospatial DB intervention decision making common- sense KB userinterface caregiveralerts wearablessensors environmentalsensors

  9. cognitive state goals activity General Model geospatial DB intervention decision making common- sense KB physical motion & position userinterface caregiveralerts wearablessensors environmentalsensors

  10. Deciding to Intervene A = system intervenes G = user actually needs help

  11. ACCESSWay-finding Assistant supported by National Institute on Disability & Rehabilitation Research DARPA IPTO

  12. The Need: Community Access for the Cognitively Disabled

  13. Problems in Using Public Transportation • Learning bus routes and numbers

  14. Problems in Using Public Transportation • Learning bus routes and numbers • Transfers, complex plans

  15. Problems in Using Public Transportation • Learning bus routes and numbers • Transfers, complex plans • Recovering from mistakes

  16. Result • Need for extensive life-coaching • Need for point-to-bus service

  17. Result • Need for extensive life-coaching • Need point-to-bus service • Isolation

  18. Current GPS Navigation Devices • Designed for drivers, not bus riders! • Should I get on this bus? • Is my stop next? • What do I do if I miss my stop? • Requires extensive user input • Keying in street addresses no fun! • Device decides which route is “best” • Familiar route better than shorter one • “Catastrophic failure” when signal is lost

  19. New Approach • User carries GPS cell phone • System infers transportation mode • Position, velocity, geographic information • Over time, system learns about user • Important places • Common transportation plans • Breaks from routine = possible user errors • Ask user if help is needed

  20. User Model ck-1 ck Cognitive mode { routine, novel, error } gk-1 gk Goal tk-1 tk Trip segment mk-1 mk Transportation mode xk-1 xk Edge, velocity, position qk-1 qk Data (edge) association zk-1 zk GPS reading Time k Time k-1

  21. Error Detection: Missed Bus Stop

  22. Prototype: Opportunity Knocks • GPS camera-phone • “Knocks” when there is an opportunity to help • Can I guide you to a likely destination? • I think you made a mistake! • This place seems important – would you photograph it?

  23. Status • User needs study • Algorithms for learning and predicting transportation behavior • Best paper award at AAAI-2004 • Proof of concept prototype • Now: user interface studies • Modality: Audio, Graphics, Tactile, ... • Guidance strategies: Landmarks, User frame of reference, Maps, ...

  24. ADL Monitoring from RFID Tag Data UW CSE Intel Research Seattle demo at Intel this afternoon

  25. Object-Based Activity Recognition • Activities of daily living involve the manipulation of many physical objects • Kitchen: stove, pans, dishes, … • Bathroom: toothbrush, shampoo, towel, … • Bedroom: linen, dresser, clock, clothing, … • We can recognize activities from a time-sequence of object touches

  26. Sensing Object Manipulation • RFID: Radio-frequency identification tags • Small • Long-lived – no batteries • Durable • Easy to deploy • Bracelet touch sensor • Wall-mount movement sensor

  27. Example Data Stream

  28. Example Activity Model

  29. Creating Models of ADLs • Hand-built • Learn from sensor data • Mine from natural-language texts • All of the above...

  30. Experiment: Morning Activities • 10 days of data from the morning routine in an experimenter’s home • 61 tagged objects • 11 activities • Often interleaved and interrupted • Many shared objects

  31. DBN with Aggregate Features 88% accuracy6.5 errors per episode

  32. Improving Robustness • Tracking fails if novel objects are used • Solution: smooth parameters over abstraction hierarchy of object types

  33. Status • Accurate tracking of wide variety ADLs • Active collaboration with Intel • Current work • Detecting user errors in ADL performance • Learning more complex ADLs • Preconditions/effects • Multi-tasking • Temporal constraints • Reminding & prompting

  34. Concluding Remarks • Research on Assisted Cognition going great guns at UW and (a few) other universities • CMU / Pitt / U Michigan (Nursebot, Autominder – M. Pollack) • Georgia Tech (Aware Home, G. Abowd) • MIT (House N, Stephen Intille)

  35. Some Thoughts on Funding • Getting funding for work in this area is currently challenging • We were fortunate once with NIDRR, but less than 1% of their budget is for research • NIH & NIA spend relatively little on caregiving research • New NIH “Roadmap” for interdisciplinary exploratory research completely leaves out caregiving! • NIN has good people, but no real money

  36. Some Thoughts on Funding • Getting funding for work in this area is currently challenging • NSF supports some of the underlying, multi-use technology, but not medically-oriented applications • Exception: helping disabled use computers • Industry support is vital, but more for collaboration than actual dollars • Good industry grant = 1 grad student • There’s a gap waiting to be filled...

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