[rehab] course design feedback (fwd)

[Leslie Fairall]

From: Hallman, Laurie
Sent: Monday, February 27, 2012 3:20 PM
To: VHA BRS National Staff Listing
Subject: FW: course design feedback



I received a request for information regarding the development of a research
project. They have a short turn around and need information preferably by
this Tuesday COB. I answered their questions but presented the problem of
only getting information from one O&M instructor and told them that I could
try to forward to our group for input. If you have time, please review the
attached questions and respond to the email address included
(hprofita at gmail.com).

Laurie



From: Hal Pro [mailto:hprofita at gmail.com]
Sent: Monday, February 27, 2012 2:36 AM
To: Hallman, Laurie
Cc: Ben Leduc-Mills
Subject: course design feedback



Hi Laurie,



Thank you again for all of your help. We would greatly appreciate it if you
could pass along our questions to other specialists to assist in our study
design for evaluation of the ioCane, a sensor-based system that attaches to
a blind cane and is used to detect low-hanging objects. We would like to
test the ioCane's effectiveness and would appreciate as much feedback and
insight about a course design as possible. I have provided a description of
the ioCane below, and have included our list of questions as an attachment.



Thank you very much!



Halley



Description of Technology

We present the design, implementation, and early results of ioCane, a
mobility aid for blind cane users that incorporates the use of ultrasonic
sensors and computer vision algorithms with the Android mobile operating
system, to provide a plug-and-play solution for the visually impaired that
has the potential to significantly enhance mobility and object avoidance
with a minimal learning curve. The system functions by taking in readings
from three seperate ultrasonic sensors placed along the cane and sending the
data to a circuit board built to interface with Android mobile devices. The
board then sends the sensor data (via Bluetooth) to our ioCane application
on the mobile phone, which determines a threshold indicating whether the
user is close to hitting an object. If so, the application vibrates
(increasing intensity with the proximity of the object) or chimes (3
different tones, dependent on the height of the object detected) to alert
the user to avoid the object. In addition, the ioCane application runs a
series of computer vision algorithms to detect and alert the user if
specific objects of interest are approaching. The sensors and board can fit
directly onto a user's existing cane, are extremely lightweight (under 400
grams), and can run off battery power. To our knowledge, the ioCane is the
first sensor-based mobility assistance system to integrate natively with a
mobile phone without any modifications necessary to either the phone or the
system, and provides a novel integration of physical sensors and computer
vision to provide navigation assistance.




-- 
Halley P Profita

Computer Science Doctoral Candidate 2015
University of Colorado - Boulder
e| hprofita at gmail.com
p| 305.439.4578



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