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.

1. Activity-based UbiComp for Health James LandayShort-Dooley ProfessorComputer Science & EngineeringUniversity of Washington* Joint work with Intel Labs3rd US-China CS Leadership SummitPeking UniversityJune 14-15, 2010 Visiting Faculty ResearcherMicrosoft Research Asia

6. Activity-based UbiCompCan Help Improve our Lives Long-lived activities in our everyday lives e.g., staying healthy, graceful aging, learning a language high-level, physical, dynamic, & high value Key elements: social, natural UIs, always at hand Hard to create successfully solely with traditional CogSci-based HCI or Art Studio-based Design

7. Importance of Physical Activity Regular physical activity is critical to physical & psychological health Spending on fitness gadgets & equipment is on the rise Rates of inactivity also rising How can we encourage people to be physically active?

8. ubifitActivity-based Application Intel/UW: Consolvo, McDonald, Landay … Problem overweight/obesity a global epidemic have hard time fitting exercise into lives Solution: Ambient feedback of activity CHI 2008, Ubicomp 2008, CHI 2009

9. 3 Main Components of UbiFit Garden communicates data about physical activities + + glanceabledisplay interactiveapplication fitness device collects data about physical activities

10. The Glanceable Display walk cardio strength flexibility this week’s goal met recent goal met

11. Fitness DeviceIntel Mobile Sensing Platform – MSP Choudhury, Lester,Borriello, LaMarca, LeGrand, … Infers physical activities & their durations, specifically walking running cycling use of elliptical trainer use of stair machine IEEE Pervasive Computing, 7(2), 2008

12. Activity Journaling any physical activity including those not inferred by the fitness device automatically manuallyon phone

14. barometric pressure

15. humidity

16. visible light

17. infrared light

18. temperature

19. temperature (a 2nd)

20. 44 khz microphone

21. compass (optional)mobile sensing platform (msp) battery

22. Robust Action Inference:Human Actions from Motion Choudhury, Lester,Borriello, Landay, Fogarty, Saponas… measure of confidencefor particular activities mean, median,range, etc. IntelMSP collect rawsensor readings calculatefeatures producemargins smooth margins intomeaningful actions Send margins to phonevia bluetooth > 95% accuracy on smartphones (Android, iPhone, Windows) for walking, running, biking, standing IEEE Pervasive Computing, 7(2), 2008

23. UbiFit Evaluation 3-week pilot field trial (n=12) shake out system & get feedback on UI/inference 3-month field trial (n=28), 3 conditions full system (n=10): interactive app + MSP + garden no MSP (n=9): interactive app + garden no garden (n=9): interactive app + MSP Results of the field trial participants with garden maintained weekly activity level over the study participants without the garden showed a significant decrease over time strong enthusiasm for a garden or similar metaphor on phone’s background participants wanted system with automatic activity inference garden

24. UbiFit Lessons LearnedOver 2 Years of Development & Testing Activity inference difficult - to collect data for, train, & tune Design -> coded system = BAD! - hard to change & iterate Evaluation time consuming - 2 full time researchers Left “mass of data on the table” - no easy way to understand

25. Activity-based UbiCompKey Challenges & New Ideas Physical actions are tedious to record & manage Build applications using action inference Natural interactions are ambiguous Improve disambiguation using dynamic context Must study in situ over extended periods Use new methods & tools to improve data collection, analysis & application prototyping

26. Analytical Design Studio Landay, Edge, Kientz, Kolko, Lee, Patel, Philipose, Ramey, Riche, Roesler, Zhao Novel Tools MyExperience – context-aware experience sampling* ActivityDesigner– design & prototyping for designers ActivityViz –visual analytics of activity data *AKA Ecological Momentary Assessment

27. Activity-Based UbiComp Summary Solve high value problems, improving our lives by using inference: actions & high level activities tools: for visualization, design, & user studies natural UIs: improve recognition using context UbiFit uses Activity-based UbiComp for Health + + @MSRA: Looking at higher level concept of wellness, mobile phone for sensing, & cultural differences

28. Activity-based UbiComp for Health James LandayShort-Dooley ProfessorComputer Science & EngineeringUniversity of Washington 3rd US-China CS Leadership Summit Peking UniversityJune 14-15, 2010 Visiting Faculty ResearcherMicrosoft Research Asia landay@cs.washington.edu http://dub.washington.edu