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Myoelectric Control of Artificial Limbs— Is There a Need to Change Focus?

Published on Jan 1, 2012
Ning Jiang31
Estimated H-index: 31
,
Strahinja Dosen23
Estimated H-index: 23
+ 1 AuthorsDario Farina80
Estimated H-index: 80
Abstract
SUMMARY AND CONCLUSIONS Myoelectric control has a great poten-tial for improving the quality of life of persons with limb deficiency. However, despite the tremendous success in obtaining almost perfect classification accuracy from EMG, its clinical and commercial impact is still limited. We have identified some of the reasons that we believe are relevant for explaining this seeming contradiction. The major-ity of current pattern classification methods do not provide simultaneous and proportional control, are not imple-mented with sensory feedback, do not adapt to the changes in EMG signal characteristics, and do not integrate other sensor modalities to allow com-plex actions. These problems hinder the possibility of using such paradigm in applications that aim at clinical and commercial use. Academic research has focused in the past decades on refining classification accuracy and has rele-gated to secondary importance the aspects outlined in this article. As such, a gap between the academia and the industry state of the art has been gener-ated unnecessarily. This gap could be filled by addressing the specific needs of intuitive myoelectric control and sys-tem robustness. With this position, we are not questioning the need of further research within pattern classification of EMG. Indeed, three of the four demands that we have identified can be imple-mented within a pattern classification paradigm. Rather, our intention is to raise the awareness for the necessity of additional parallel research efforts toward issues whose importance for practical implementations has been underestimated.
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