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Keynote for AAAI Fall Symposium, AI in Eldercare track.

Keynote for AAAI Fall Symposium, AI in Eldercare track.

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  • 1. Do try this at home! Living with In-Home Health Monitoring Systems Beth Logan Intel Digital Health Product Research and Incubation With collaborators from Intel Research Seattle, MIT and University of Washington
  • 2. Where would you rather live as you age?
    • Or in your home?
      • Likely cheaper with more privacy
    • In a nursing home?
      • More companionship
      • Less privacy
      • Far away?
      • More expensive
      • Easy monitoring
  • 3. Barriers to Staying Home Alone
    • Disabilities
      • Unable to perform ADLs
      • Cognition problems
    • Fear of worst case scenarios
      • Falls, fires, heat/air-conditioning failures, break-ins
    • Burden on caregivers too great
      • Constant visits or phone calls needed
      • Can sensor-based activity monitoring increase quality of life for elders home alone and reduce the effort of their caregivers?
  • 4. ADL Monitoring Wish List
    • Detect basic ADLs with good accuracy
      • Eating, movement, bathroom use, etc
      • Possibly detect emergencies
    • Minimal invasion of privacy
    • Easy installation
    • Sensors are unobtrusive, work flawlessly and need little maintenance
    • Wearables are a pleasure to wear and easy to maintain
    • User understands enough about the system to fix small problems
    • Remote maintenance for upgrades and major problems
  • 5. Baby Steps
    • Monitor people’s activities in a sensorized home
    • Questions
      • Can activities be detected with useful accuracy?
      • Do people like/mind living in a sensorized home?
  • 6. MIT House_n PlaceLab
      • Condo near MIT
      • Hundreds of sensors
      • Video for annotation
      • Subjects live undisturbed for weeks or months
        • Data uploaded weekly
        • Residents have ‘veto rights’
  • 7. Built-In Sensors
    • 206 inputs total
      • 101 wired reed switches
      • 14 water flow
      • 37 current
      • 36 temperature
      • 1 pressure
      • 10 humidity
      • 1 gas
      • 6 light
    Barometric pressure Humidity Temperature IR video camera Color video camera Top-down camera Light sensor Microphone Switches to detect open/close Temperature Water flow Current
  • 8. RFID Tags and Bracelet
    • 435 tags installed throughout condo
    RFID Bracelet RFID Tags
  • 9. Motion Based Sensors
    • 281 inputs total
      • 265 on-object accelerometers
      • 2x3axis on-body accelerometers
      • 10 infra-red motion detectors
  • 10. Typical study Joint with Intel 2007
    • Studied a young married couple living for 10 weeks in the PlaceLab
    • 104 hours of data annotated
      • Covers 15 days from middle of experiment
      • Chose periods when male subject was home
    • Independent annotator
      • Annotated male’s activities from the video
      • Used a 98 activity ontology
      • Limited/no video in bathrooms and bedroom; still had audio
  • 11. Living in a sensorized home
    • Participants maintained as normal routine as possible
    • Brought many possessions into the apartment to feel “at home”
    • Wore some sensors
      • Both subjects wore accelerometers on wrist, hips and thigh
      • Male subject wore a bulky RFID bracelet
    • Installed some sensors
  • 12. Participants’ reactions
    • “ We weren’t as conscious as I thought we would be, it was actually kind of natural being here…I didn’t notice some things as much as I thought I would, like the cameras.” Male subject
    • “ I wasn’t bothered by it, really at all, I thought I might get weirded out every once in a while, but there were very few times where I was totally tired of being in the project, and I felt pretty comfortable here” Female subject
  • 13. What do people do at home?
    • Most and least observed activities in 104h annotated set, male subject only
    Activity Total Observed Time Using a computer 1866 min Background listening to music or radio 813 min Actively watching tv or movies 732 min Sleeping deeply 728 min Reading book/paper/magazine 359 min Preparing a snack 44 s Leaving the home 42 s Making the bed 34 s Washing hands 24 s Drying dishes 6 s
  • 14. Activity Recognition Results Excellent  Poor  Sensors
  • 15. Take-aways (more detail in the paper)
    • People behave unpredictably “in the wild”
      • Revealed by our long-term study with unaffiliated subjects and annotator
      • Interrupted activities, multi-tasking, activities in multiple and unexpected locations
    • Many activities are short and infrequent
      • Collecting sufficient “real life” data very challenging
    • Location “good enough” for recognizing many simple activities
      • Likely not good enough for fine-grained activities
    • Even with 100s of sensors, our sensor density was sometimes insufficient
      • Opportunity for new sensors
  • 16. Next Steps
    • Move to in-home installed systems
      • Address lack of data by studying ways users can give feedback to an adaptive system
        • Ongoing work with MIT
      • Shift focus to older subjects
        • Collaboration with Intel Research Seattle
        • (rest of this talk)
    • FYI PlaceLab annotated dataset available online
      • Sensor data, activity ontology, annotations
      • http://architecture.mit.edu/house_n/data/PlaceLab/PLCouple1.htm
  • 17. Technologies for Long-Term Care (TLC) Joint with Intel Research Seattle
    • Sensors in elders’ homes track activities and generate a summary display for caregivers
    RFID bracelet and RFID tags “ shake” sensors Display
  • 18. Display Detail 5 day trend Eating and ambulation timeline Today’s grooming and medication status Customizable photo background
  • 19. How to have an experiment in your home…
    • My official roles
      • Inference Lead responsible for algorithms to detect activities
      • Middleware co-designer and contributor
    • My unofficial roles
      • Chief QA engineer
      • Chief guinea pig
        • Only way to get the data I needed to design the inference!
  • 20.
    • 12 shake sensors
    • 10 RFID tags and RFID bracelet
    • 1 aggregator (laptop) with attached 802.15.4 antenna
    • Wore a phone camera around neck for 1 week to get ground truth
    • Kept display at work to easily show demos
    Home Deployment Sep 2007 – Oct 2008
  • 21. Technical Experience
    • Ease of install and setup 9/10
      • Some issues with sensors falling off at first
      • Good debugging beforehand paid off
      • Some tweaking of inference
    • Maintenance 6/10
      • Constantly changing shake sensor batteries – typically one per week
      • Bracelet needed as much attention as a cell phone
      • Some work to keep the Ceiva display up and running
      • Aggregator had 0 downtime due to bugs or failures!
    • Ease of uninstall 10/10
      • no marks anywhere!
  • 22. User Experience
    • Aesthetics 8/10
      • Over time found a way to hide most sensors but hated having the laptop and the antenna in the dining room
    • Ease of use
      • Barely noticed shake sensors 9/10
      • Hated the bracelet after about a month 1/10
        • Fragile, uncomfortable, needed constant attention, too ugly to wear around guests
    • Friends and family 10/10 (non-bracelet)
      • Many people didn’t even notice the experiment until I pointed it out!
        • I have plenty of computers
        • Only wore the bracelet around my boyfriend
    • Privacy
      • No issues with the main experiment 9/10
      • Hated wearing camera phone for ground truth 0/10
        • bulky and felt very invasive
    Antenna and laptop
  • 23. My Negative Bias
    • I was developer, maintainer and user
      • Typically if there was a problem, I owned it
      • Felt like I was bringing work home
    • I had the display screen at work, not home
      • Much more public place
    • I don’t (yet!) need TLC
      • A burden, especially maintaining the bracelet
  • 24. Findings from TLC Elder Study 2008
    • Interviews from 4 elders, 2 formal, 2 family caregivers in Seattle area
    • Roughly 10,000 hrs of data
    • 3.5 days of camera ground truth
    • Experience from 9 installs
    • Still finishing the study
      • 14 elders total but don’t expect major changes to findings
  • 25. Finding: TLC is Useful!
    • “ I think it’s a marvelous thing… my sister will call me up and say “I don’t have any apples, why haven’t you eaten ?” Elder
    • “ He brushes his teeth more often now because I can keep track. That wasn't something I really did before.” Caregiver
    • “ [T]here's footprints all over it-- so she wasn't sleeping last night … So, I won't bother her for a while.” Caregiver
  • 26. Finding: TLC is Easy to Use
    • Elder’s ability to tolerate and manage sensor bracelet was a concern
    • Feedback from elders is reassuring:
      • “ Ain't nothing to it. Just takes 30 seconds. I take it off when I leave the house, but sometimes I won't take it off for a week…”
    • TLC elders wear bracelets on over 95% of days.
  • 27. Finding: TLC is Delightful
    • Attractive design and appealing backgrounds are a key part of the displays
    • “ I love it. It's interesting… I wish I could get the cat to come back more. It comes only every week and a half.” Elder
  • 28. Finding: TLC is Well Behaved
    • False-positive rates for activity recognition are under 10%
      • Very simple inference (Naïve Bayes + rules)
      • Acceptable for human-processed output
    • Installation time is under 2 hours per user
    • Failures are as expected from pilots
      • Picture frame backup
      • Battery drain
      • Bracelet antenna damage
  • 29. Are we there yet?
    • We’re making good progress!
    • Heartening to see our system was well received and even liked
    • Debugging at home
        • Great way to increase robustness and get useful feedback instead of focusing on technical problems
        • Sometimes painful but a good way to show faith in a system
        • Definitely not 100% predictive of real users
  • 30. Where to next?
    • We are not far away from useful systems!
      • Already some products on the market
    • Biggest challenges
      • Installation and maintenance
      • Lack of real-world data
        • Work on adaptive systems
        • Make anonymized data public?
        • CHI2009 workshop "Developing Shared Home Behavior Datasets”
      • Move beyond sensing to actuation
  • 31. Collaborators
    • Intel
      • Matthai Philipose, TLC project lead
      • Jennifer Healey, Brad Needham, Ken Lafond, Adam Rea, Polly Powledge, Beverly Harrison, Jean-Manuel Van Thong, Scott Blackwell.
    • MIT House_n
      • Stephen Intille, Director of Technology
      • Emmanuel Munguia-Tapia, Jennifer Beudin
    • University of Washington School of Public Health
      • Sheri Reder, Clinical Instructor
      • Susan Hedrick, Professor
  • 32. Backup