1. Mobile Sensing: Leveraging Mobile Phones to Support Personal, Community, and Participatory Sensing Nithya Ramanathan Collaborating Faculty: Jeff Burke, Deborah Estrin, Mark Hansen, Mani Srivastava, Ruth West UCLA Center for Embedded Networked Sensing UCLA Center for Research in Engineering, Media and Performance Staff and Graduate Students: Faisal Alquaddoomi, Betta Dawson, Jeff Goldman, Eric Howard, August Joki, Donnie Kim, Vinayak Naik, Min Mun, Nicolai Petersen, Sasank Reddy, Jason Ryder, Vids Samanta, Katie Shilton, Nathan Yau UCLA Departments of Computer Science, Electrical Engineering, Statistics CENS Urban Sensing collaborators also include: Mark Allman, Dana Cuff, Jerry Kang, Vern Paxson, Fabian Wagmister, CENS Urban Sensing funding sources include: NSF CRI, NeTS-FIND, and OIA; Cisco, Nokia, Schematic, Sun, Walt Disney Imagineering R&D http://urban.cens.ucla.edu

2. Text Entry Imagers Audio Location (GPS) Accelerometer Bluetooth Network Connectivity What can one person do with this powerful tool?

3. Reddy, Samanta, Burke, Estrin, Hansen, Srivastava Presentation Presentation Visualization Processing Raw Data Can real-time feedback about our actions change our behavior?

7. Data Campaigns Reddy, Samanta, Burke, Estrin, Hansen, Srivastava Phones enhance participation Make it.. Easier to collect more data Data is more credible and verifiable

8. Data Campaigns Reddy, Samanta, Burke, Estrin, Hansen, Srivastava Phones enhance participation Make it.. Easier to collect more data Data is more credible and verifiable No Technological Innovation

9. Image and activity data to study pollution exposure http://www-ramanathan.ucsd.edu/ProjectSurya.html In collaboration with UCSD, SRU and TERI in India Characterize Outdoor Activities Infer Duration of Exposure Collect Indoor Pollution Levels Bluetooth temperature sensor Phone + GPS, accelerometer Special soot filter

10. Active Image Collection for citizen science http://www.windows.ucar.edu/citizen_science/budburst In collaboration with the ongoing citizen science initiative known as BudBurst

11. Reflecting on and learning from personal mobility Cyclesense combines location data and users’ photos to give bikers daily feedback and suggestions on the quality and safety of their commutes. In PEIR , the combination of location, time, and activity are automatically interpreted using regional air quality models to estimate participants’ exposure to particulate matter. http://peir.cens.ucla.edu/ http://urban.cens.ucla.edu/projects/cyclesense/

12. Audio and location collection to recall family interactions http://urban.cens.ucla.edu/projects/familydynamics/ http://www.kt.tu-cottbus.de/speech-analysis/ http://urban.cens.ucla.edu/projects/familydynamics/ In collaboration with the Semel Institute

13. While off-the-shelf components can be used to implement data campaigns, there is still room for improvement….

15. Design to Maximize Trust and Participation Design Theme : Involve rather than burden the user by designing systems that are easy to use and understand. Design Theme : Validate data.

16. http://urban.cens.ucla.edu/

17. PEIR Press Release

18. Passive Image Collection for Diet Recall Studies http://urban.cens.ucla.edu/projects/dietsense/ In collaboration with the public health department at UCLA

19. Data Analysis and Using external data streams Example: Estimating Pollution without Pollution Sensors Lifelong damage found in 13-year study of 3,600 Southland youngsters living within 500 yards of a highway. The Los Angeles Times, 1/26/07 Houston, Winer et al Source: McConnell et al. Traffic, Susceptibility, and Childhood Asthma. Environ Health Perspect 114:766–772 (2006)