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Alex Edgcomb Frank Vahid University of California, Riverside Department of Computer Science

Feature Extractors for Integration of Cameras and Sensors during End-User Programming of Assistive Monitoring Systems. ?. Alex Edgcomb Frank Vahid University of California, Riverside Department of Computer Science. Motion sensor. Sensors and actuators in MNFL [1] for end-user programming.

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Alex Edgcomb Frank Vahid University of California, Riverside Department of Computer Science

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  1. Feature Extractors for Integration of Cameras and Sensors during End-User Programming of Assistive Monitoring Systems ? Alex Edgcomb Frank Vahid University of California, Riverside Department of Computer Science Motion sensor

  2. Sensors and actuators in MNFL [1] for end-user programming [1] Edgcomb, A. and F. Vahid. MNFL: The Monitoring and Notification Flow Language for Assistive Monitoring. Proceedings 2nd ACM International Health Informatics Symposium, 2012. Miami, Florida. [2] Philips, B. and H. Zhao. Predictors of Assistive Technology Abandonment. Assistive Technology, Vol. 5.1, 1993, pp. 36-45. [3] Riemer-Reiss, M. Assistive Technology Discontinuance. Technology and Persons with Disabilities Conference, 2000. Outdoor motion sensor “Person at door” LED lights in house Doorbell • Assistive monitoring • User customizability essential [2][3] “Person at door” Alex Edgcomb, UC Riverside

  3. Expanding the previous example Outdoor motion sensor “Person at door” LED lights in house Doorbell Light sensor “Person at door” Porch light Alex Edgcomb, UC Riverside

  4. Webcams are cheap Alex Edgcomb, UC Riverside

  5. Webcams can do more than sensors Identify person at front door Can do same as some sensors Motion sensor Light sensor In room for extended time Alex Edgcomb, UC Riverside Fall down at home

  6. Problem: Integration of webcams and sensors Outdoor motion sensor Commercial approach: ? Homesite Alex Edgcomb, UC Riverside

  7. Solution: Feature extractor 100 0 92 Integer stream output Extract some feature Video stream input Alex Edgcomb, UC Riverside

  8. Identify person at door in MNFL Outdoor motion sensor Alex Edgcomb, UC Riverside

  9. Person in room for extended period of time in MNFL Video’s YouTube link Alex Edgcomb, UC Riverside

  10. Many feature extractors are possible Alex Edgcomb, UC Riverside

  11. Are feature extractors usable by lay people? Two usability trials. • 51 participants • Trials required as 1st lab assignment • Non-engineering/non-science students at UCR Alex Edgcomb, UC Riverside

  12. Participant reference materials • One-minute video showing how to spawn and connect blocks. • Overview picture Alex Edgcomb, UC Riverside

  13. Example challenge problem actual participant solution Alex Edgcomb, UC Riverside

  14. Trial 1: Increasingly challenging feature extractor problems 25 participants Alex Edgcomb, UC Riverside

  15. Trial 2: Feature extractor vs logic block 26 participants Alex Edgcomb, UC Riverside

  16. Conclusions • Feature extractors • Elegant integration of cameras and sensors • Quickly learnable by lay people • Future work • Develop additional feature extractor blocks • Trade-off analysis between privacy, communication, and computation Alex Edgcomb, UC Riverside

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