EndUser Programming of Assistive Monitoring Systems Alex Edgcomb Frank Vahid University of California Riverside Department of Computer Science 1 of 16 Motion sensor Sensors and actuators in MNFL 1 for enduser programming ID: 145465
Download Presentation The PPT/PDF document "Feature Extractors for Integration of Ca..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Feature Extractors for Integration of Cameras and Sensors during End-User Programming of Assistive Monitoring Systems
Alex EdgcombFrank VahidUniversity of California, RiversideDepartment of Computer Science
1 of 16
?
Motion sensorSlide2
Sensors and actuators in MNFL [1] for end-user programming
Alex Edgcomb, UC Riverside2 of
16
“Person at door”
LED lights in house
“Person at door”
Outdoor motion sensor
Doorbell
Assistive monitoring
User customizability essential [2][3]
[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.Slide3
Porch light
LED lights in house
Expanding the previous example
Alex Edgcomb, UC Riverside
3
of
16
“Person at door”
“Person at door”
Outdoor motion sensor
Doorbell
Light sensorSlide4
Webcams are cheap
4 of 16Alex Edgcomb, UC RiversideSlide5
Webcams can do more than sensors
Fall down at home
In room for extended time
Can do same as some sensors
Motion sensor
Light sensor
5
of
16
Alex Edgcomb, UC Riverside
Identify person
at front doorSlide6
Problem: Integration of webcams and sensors
6 of 16
Homesite
Commercial approach:
Alex Edgcomb, UC Riverside
?
Outdoor motion sensorSlide7
Solution: Feature extractor
7 of 16
92
Integer stream output
0
100
Alex Edgcomb, UC Riverside
Extract some feature
Video stream inputSlide8
Identify person at door in MNFLAlex Edgcomb, UC Riverside
8 of 16
Outdoor motion sensorSlide9
Person in room for extended period of time in MNFL
9 of 16Video’s YouTube linkAlex Edgcomb, UC RiversideSlide10
Many feature extractors are possible
10 of 16Alex Edgcomb, UC RiversideSlide11
Are feature extractors usable by lay people? Two usability trials.
51 participantsTrials required as 1st lab assignmentNon-engineering/non-science students at UCR
11 of 16Alex Edgcomb, UC RiversideSlide12
Participant reference materials
One-minute video showing how to spawn and connect blocks.Overview picture
12
of 16
Alex Edgcomb, UC RiversideSlide13
Example challenge problem
13 of 16Alex Edgcomb, UC Riverside
actual participant solutionSlide14
Trial 1: Increasingly challenging feature extractor problems
25 participants14 of 16
Alex Edgcomb, UC RiversideSlide15
Trial 2: Feature extractor vs logic block
26 participants15 of 16Alex Edgcomb, UC RiversideSlide16
ConclusionsFeature extractors
Elegant integration of cameras and sensorsQuickly learnable by lay peopleFuture workDevelop additional feature extractor blocksTrade-off analysis between privacy, communication, and computation16 o f 16
Alex Edgcomb, UC Riverside