IEEE Robotics & Automation Magazine - December 2017 - 111
features was investigated. To address another cause of
upper-limb prostheses abandonment, i.e., the difficulty of
use, pattern recognition was employed. By utilizing pattern
recognition, the number of controllable motion classes
may be increased. This eliminates the need for complex
on-off combinations for the control of more than two
motion classes.
To examine the practicality of the proposed techniques,
a portable device was developed. The system that was
introduced in this study can be used in daily life without
requiring modifications. For future work, we plan to
increase the practicality of the device by reducing the size
and weight of the electronics. This can be achieved by
implementing a design that replaces the processing unit
used in this prototype, BBB, by microprocessors such as
PSoC Cypress chips. Moreover, to evaluate system performance in unconstrained environments, we plan either to
develop a labeling method for the real-time use of the
device that does not require user involvement or to investigate unsupervised learning methods.
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Chakaveh Ahmadizadeh, MENRVA Research Group, Simon
Fraser University, Burnaby, British Columbia, Canada.
E-mail: cahmadiz@sfu.ca.
Lukas-Karim Merhi, MENRVA Research Group, Simon Fraser
University, Burnaby, British Columbia, Canada. E-mail:
lma3@sfu.ca.
Brittany Pousett, Barber Prosthetic Clinic, Vancouver, British
Columbia, Canada. E-mail: brittany@barberprosthetics.com.
Sohail Sangha, MENRVA Research Group, Simon Fraser University, Burnaby, British Columbia, Canada. E-mail: sohails@
sfu.ca.
Carlo Menon, MENRVA Research Group, Simon Fraser University, Burnaby, British Columbia, Canada. E-mail:
cmenon@sfu.ca.
DECEMBER 2017
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