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Please note there was a problem with the upload. The file is a pdf, but the file ending is missing. Manually opening the file in a pdf reader should work. Otherwise feel free to contact me.
\n\nAbstract
\n\nIndividuals with upper-limb deficiency who are fitted
\nwith a prosthesis are normally trained in the use of such
\ndevice. This is even true for individuals who are fitted with a
\nmyoelectric prosthesis that uses control algorithms based on
\npattern-recognition, despite the intent of pattern-recognition
\ncontrol of exploiting “intuitive” phantom movements.
\nConventionally, training individuals for pattern-recognition
\ncontrol usually involves an expert who guides the user to
\nproduce electromyogram (EMG) signals that optimize
\npattern recognition. In the training the individual is
\nstimulated to adapt their EMG signals as to make them more
\ndistinct in terms of the resulting patterns. To achieve this, for
\ninstance, small movements can be added to the basic pattern,
\nsuch as flexing the little finger during open hand. Although
\ntraining improves online accuracy it still involves
\nconsiderable trial and error. Moreover, expert guidance is
\ncurrently done based on visual perusal of EMG patterns or
\nfeatures thereof and not based on specific metrics
\ncharacterizing those EMG signal patterns. Rather than using
\nintuitive phantom movements for control, we instead propose
\nto use those phantom movements which are most distinct in
\nterms of EMG. To find the set of phantom movements that
\nprovides the most distinct EMG activation patterns, we
\npropose to use a serious game. Using a game, we can train
\nindividuals to make EMG patterns distinct while performing
\nthem in a robust manner. This game is controlled using the
\nEMG captured from 8 electrodes positioned around the
\nforearm. Inspired by the work of Radhakrishnan et. al and
\nPistohl et. al, the EMG from each electrode is mapped to a
\ndirection of the game avatar in the 2D environment. We
\nhypothesize that this training will make individuals utilize
\ntheir EMG activation space to a greater extent and become
\nbetter at generating only EMG activity at specific electrode
\nsites so that patterns are more distinct.
\nWe are currently conducting an experiment in which 4
\nexperimental groups receive different kinds of training.
\nGroup 1 receives conventional training without coaching.
\nGroup 2 receives conventional training with feedback. Group
\n3 receives training with the proposed serious game and group
\n4 receives training without any feedback (control). The
\nlearning effects between groups are analysed using the
\nmetrics proposed by Bunderson et al. and the motion test.
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