Dealing with Changing Contexts in Myoelectric Control
Myoelectric prostheses approximate the motion and
flexibility of biological limbs, especially when compared to
their mechanical counter-parts. Machine learning enhances the
functionality of these devices; however, in an ever-changing
environment, the effectiveness of conventional approaches is
impeded. We introduce Partition Tree Learning—a method
for learning predictions in an ongoing fashion whilst being
able to identify and adapt to new contexts automatically.
We compare the performance of PTL to that of a stochastic
gradient descent learner on a stream of data from a participant
actuating a myoelectrically controlled robot arm. In a
consistent context both learners’ predictions are comparable.
After a context switch, PTL is able to adapt to the change and
outperform the gradient descent learner. These preliminary
results indicate that PTL may effectively deal with change
in real-world prosthetic use, lending its ability to learn over
varying situations to the constantly changing environment of
powered prosthetics.
Citation
A. Koop,
A. Kearney,
M. Bowling,
P. Pilarski.
"Dealing with Changing Contexts in Myoelectric Control". Myoelectric Control Symposium, pp 113-116, August 2014.
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In Conference |
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MEC |
BibTeX
@incollection{Koop+al:MEC14,
author = {Anna Koop and Alex Kearney and Michael Bowling and Patrick M.
Pilarski},
title = {Dealing with Changing Contexts in Myoelectric Control},
Pages = {113-116},
booktitle = {Myoelectric Control Symposium},
year = 2014,
}Last Updated: January 19, 2021
Submitted by Sabina P 
