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 EdgcombFrank Vahid

University of California, RiversideDepartment of Computer Science

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?Motion sensor

Sensors and actuators in MNFL [1] for end-user programming

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“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.

Porch light

LED lights in house

Expanding the previous example

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“Person at door”

“Person at door”

Outdoor motion sensor

Doorbell

Light sensor

Webcams are cheap

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Webcams can do more than sensors

Fall down at home

In room for extended time

Can do same as some sensors

Motion sensorLight sensor

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Identify personat front door

Problem: Integration of webcams and sensors

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Homesite

Commercial approach:

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?

Outdoor motion sensor

Solution: Feature extractor

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92Integer stream

output

0

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Extract some feature

Video stream input

Identify person at door in MNFL

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Outdoor motion sensor

Person in room for extended period of time in MNFL

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Alex Edgcomb, UC Riverside

Many feature extractors are possible

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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

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Programming experience(1- little/none; 5- a lot)

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Participant reference materials

• One-minute video showing how to spawn and connect blocks.

• Overview picture

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Example challenge problem

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actual participant solution

Trial 1: Increasingly challenging feature extractor problems

25 participants14 of 16

10 9 7-8 5-6 3-4 1-2 00%

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Fall detector (easy) Visitor detector (medium)

Leave at night detector (hard)

Rubric score

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Trial 2: Feature extractor vs logic block

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Visitor detector (feature)

Emergency button (logic)

Rubric score

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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 computation16 o f 16Alex Edgcomb, UC Riverside