Poster Open Access
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Individuals with upper-limb deficiency who are fitted
with a prosthesis are normally trained in the use of such
device. This is even true for individuals who are fitted with a
myoelectric prosthesis that uses control algorithms based on
pattern-recognition, despite the intent of pattern-recognition
control of exploiting “intuitive” phantom movements.
Conventionally, training individuals for pattern-recognition
control usually involves an expert who guides the user to
produce electromyogram (EMG) signals that optimize
pattern recognition. In the training the individual is
stimulated to adapt their EMG signals as to make them more
distinct in terms of the resulting patterns. To achieve this, for
instance, small movements can be added to the basic pattern,
such as flexing the little finger during open hand. Although
training improves online accuracy it still involves
considerable trial and error. Moreover, expert guidance is
currently done based on visual perusal of EMG patterns or
features thereof and not based on specific metrics
characterizing those EMG signal patterns. Rather than using
intuitive phantom movements for control, we instead propose
to use those phantom movements which are most distinct in
terms of EMG. To find the set of phantom movements that
provides the most distinct EMG activation patterns, we
propose to use a serious game. Using a game, we can train
individuals to make EMG patterns distinct while performing
them in a robust manner. This game is controlled using the
EMG captured from 8 electrodes positioned around the
forearm. Inspired by the work of Radhakrishnan et. al and
Pistohl et. al, the EMG from each electrode is mapped to a
direction of the game avatar in the 2D environment. We
hypothesize that this training will make individuals utilize
their EMG activation space to a greater extent and become
better at generating only EMG activity at specific electrode
sites so that patterns are more distinct.
We are currently conducting an experiment in which 4
experimental groups receive different kinds of training.
Group 1 receives conventional training without coaching.
Group 2 receives conventional training with feedback. Group
3 receives training with the proposed serious game and group
4 receives training without any feedback (control). The
learning effects between groups are analysed using the
metrics proposed by Bunderson et al. and the motion test.