Activity-based UbiComp for Health<br />James LandayShort-Dooley ProfessorComputer Science & EngineeringUniversity of Washi...
Activity-based UbiCompCan Help Improve our Lives<br />Long-lived activities in our everyday lives <br />e.g., staying heal...
Importance of Physical Activity<br />Regular physical activity is critical to physical & psychological health<br />Spendin...
ubifitActivity-based Application<br />Intel/UW: Consolvo, McDonald, Landay …<br />Problem<br />overweight/obesity a global...
3 Main Components of UbiFit Garden<br />communicates data about physical activities<br />+<br />+<br />glanceabledisplay<b...
The Glanceable Display<br />walk<br />cardio<br />strength<br />flexibility<br />this week’s goal met<br />recent goal met...
Fitness DeviceIntel Mobile Sensing Platform – MSP<br />Choudhury, Lester,Borriello, LaMarca,<br />LeGrand, …<br />Infers p...
Activity Journaling<br />any physical activity including those not inferred by the fitness device<br />automatically<br />...
Inside the Intel MSP<br />2<br />9 sensors<br /><ul><li>3d accelerometer
barometric pressure
humidity
visible light
infrared light
temperature
temperature (a 2nd)
44 khz microphone
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Activity-based UbiComp for Health

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Describes the UbiFit project and how it relates to the general idea of activity-based computing. UbiFit was a join collaboration between Intel Labs Seattle and the University of Washington. The project attempts to use low-cost sensing, inference, and feedback to allow people to stay physically active. This project is an example of the larger thrust of activity-based ubiquitous computing.

This was presented at the 3rd U.S.-China Computer Science Leadership Summit at Peking University, Beijing China on June 14, 2010.

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  • [20 minute goal, took 23 minutes]
  • For example, how would we design and test for an applications to encourage people to take greener transit instead of driving their car…
  • Or how to allow elders to stay in their own homes and connected to their families instead of moving into care facilities….
  • Orhow to encourage busy people to get more exercise in their lives….
  • Mobile technology, IE THE MOBILE PHONE, is a key component in enabling many of these scenarios. We now have powerful sensing, computing, and communications platform with us everywhere we go. How can we use this to improve our lives?
  • Ecological momentary assessment
  • Activity-based UbiComp for Health

    1. 1. Activity-based UbiComp for Health<br />James LandayShort-Dooley ProfessorComputer Science & EngineeringUniversity of Washington* Joint work with Intel Labs3rd US-China CS Leadership SummitPeking UniversityJune 14-15, 2010<br />Visiting Faculty ResearcherMicrosoft Research Asia<br />
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    5. 5.
    6. 6. Activity-based UbiCompCan Help Improve our Lives<br />Long-lived activities in our everyday lives <br />e.g., staying healthy, graceful aging, learning a language<br />high-level, physical, dynamic, & high value<br />Key elements: social, natural UIs, always at hand<br />Hard to create successfully solely with traditional CogSci-based HCI or Art Studio-based Design<br />
    7. 7. Importance of Physical Activity<br />Regular physical activity is critical to physical & psychological health<br />Spending on fitness gadgets & equipment is on the rise<br /> Rates of inactivity also rising<br />How can we encourage people to be physically active?<br />
    8. 8. ubifitActivity-based Application<br />Intel/UW: Consolvo, McDonald, Landay …<br />Problem<br />overweight/obesity a global epidemic<br />have hard time fitting exercise into lives<br />Solution: Ambient feedback of activity<br />CHI 2008, Ubicomp 2008, CHI 2009<br />
    9. 9. 3 Main Components of UbiFit Garden<br />communicates data about physical activities<br />+<br />+<br />glanceabledisplay<br />interactiveapplication<br />fitness device<br />collects data about physical activities<br />
    10. 10. The Glanceable Display<br />walk<br />cardio<br />strength<br />flexibility<br />this week’s goal met<br />recent goal met<br />
    11. 11. Fitness DeviceIntel Mobile Sensing Platform – MSP<br />Choudhury, Lester,Borriello, LaMarca,<br />LeGrand, …<br />Infers physical activities & their durations, specifically<br />walking<br />running<br />cycling<br />use of elliptical trainer<br />use of stair machine<br />IEEE Pervasive Computing, 7(2), 2008<br />
    12. 12. Activity Journaling<br />any physical activity including those not inferred by the fitness device<br />automatically<br />manuallyon phone<br />
    13. 13. Inside the Intel MSP<br />2<br />9 sensors<br /><ul><li>3d accelerometer
    14. 14. barometric pressure
    15. 15. humidity
    16. 16. visible light
    17. 17. infrared light
    18. 18. temperature
    19. 19. temperature (a 2nd)
    20. 20. 44 khz microphone
    21. 21. compass (optional)</li></ul>mobile sensing platform (msp)<br />battery<br />
    22. 22. Robust Action Inference:Human Actions from Motion<br />Choudhury, Lester,Borriello, Landay, Fogarty, Saponas…<br />measure of confidencefor particular activities<br />mean, median,range, etc.<br />IntelMSP<br />collect rawsensor readings<br />calculatefeatures<br />producemargins<br />smooth margins intomeaningful actions<br />Send margins to phonevia bluetooth<br /> > 95% accuracy on smartphones (Android, iPhone, Windows)<br />for walking, running, biking, standing<br />IEEE Pervasive Computing, 7(2), 2008<br />
    23. 23. UbiFit Evaluation<br />3-week pilot field trial (n=12)<br />shake out system & get feedback on UI/inference<br />3-month field trial (n=28), 3 conditions<br />full system (n=10): interactive app + MSP + garden<br />no MSP (n=9): interactive app + garden<br />no garden (n=9): interactive app + MSP<br />Results of the field trial<br />participants with garden maintained weekly activity level over the study<br />participants without the garden showed a significant decrease over time<br />strong enthusiasm for a garden or similar metaphor on phone’s background<br />participants wanted system with automatic activity inference<br />garden<br />
    24. 24. UbiFit Lessons LearnedOver 2 Years of Development & Testing<br />Activity inference difficult <br /> - to collect data for, train, & tune<br />Design -> coded system = BAD!<br />- hard to change & iterate<br />Evaluation time consuming<br />- 2 full time researchers<br />Left “mass of data on the table”<br /> - no easy way to understand<br />
    25. 25. Activity-based UbiCompKey Challenges & New Ideas<br />Physical actions are tedious to record & manage<br />Build applications using action inference<br />Natural interactions are ambiguous<br />Improve disambiguation using dynamic context<br />Must study in situ over extended periods<br />Use new methods & tools to improve data collection, analysis & application prototyping<br />
    26. 26. Analytical Design Studio<br />Landay, Edge, Kientz, Kolko, Lee, Patel, Philipose, Ramey, Riche, Roesler, Zhao<br />Novel Tools<br />MyExperience – context-aware experience sampling*<br />ActivityDesigner– design & prototyping for designers<br />ActivityViz –visual analytics of activity data<br />*AKA Ecological Momentary Assessment<br />
    27. 27. Activity-Based UbiComp Summary<br />Solve high value problems, improving our lives by using<br />inference: actions & high level activities<br />tools: for visualization, design, & user studies<br />natural UIs: improve recognition using context<br />UbiFit uses Activity-based UbiComp for Health<br />+<br />+<br />@MSRA: Looking at higher level concept of wellness, mobile phone for sensing, & cultural differences<br />
    28. 28. Activity-based UbiComp for Health<br />James LandayShort-Dooley ProfessorComputer Science & EngineeringUniversity of Washington 3rd US-China CS Leadership Summit Peking UniversityJune 14-15, 2010<br />Visiting Faculty ResearcherMicrosoft Research Asia<br />landay@cs.washington.edu<br />http://dub.washington.edu<br />

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